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Enhanced properties of low-density polyethylene composites reinforced with Bridelia ferruginea fibers: effect of low temperature fiber acetylation

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Discover Polymers 2024年第1期1卷 1-15页

作者： Fasanya, Opeoluwa O. Obada, David O. Okoduwa, Ugochi J. Adesina, Olalekan B. Winful, Elizabeth Audu, Ephraim A. Abdulkadir, Jibrin Obidah, Timothy Y. Adamun, Simon I. Barminas, Jeffrey T. Industrial and Environmental Pollution Department National Research Institute for Chemical Technology Zaria Nigeria Department of Mechanical Engineering and Africa Centre of Excellence on New Pedagogies in Engineering Education Ahmadu Bello University Zaria Nigeria Multifunctional Materials Laboratory Shell Office in Mechanical Engineering Ahmadu Bello University Zaria Nigeria Mathematical Modelling and Intelligent Systems for Health and Environment (MISHE) Research Group School of Science Atlantic Technological University Sligo Ireland Scientific and Industrial Research Department National Research Institute for Chemical Technology Zaria Nigeria

In this study, lignocellulosic fibres were extracted from Bridelia ferruginea (BF), a shrub which can be found in sub-Saharan Africa with a view to probing the potential application of the fibres. The chemical properties of the BF were chemically modified by acetylation to evaluate the potential applications of the fibre reinforced polymer composites. The unacetylated and acetylated fibres were characterized using Scanning Electron Microscopy (SEM), Fourier Transform Infrared Spectroscopy (FTIR), X-Ray Diffraction (XRD) and Thermogravimetric Analysis (TGA) to compare thermal and crystallographic properties after acetylation over different lengths of time. The acetylated fibres with the most promising properties were used for the fabrication of a polymeric composite. The mechanical properties of the fibre-reinforced polymer composites were studied by hardness and dynamic mechanical measurements. Four types of polymeric samples were prepared for the mechanical measurements: unreinforced polymer (control) polyester, ATBF/LDPE, ACEBF3/LDPE, ACEBF/LDPE. The SEM micrographs revealed that acetylation led to defibrillation. FTIR peaks at 1250 cm−1 and 1371 cm−1 confirmed that acetylation had occurred. The fibres possessed high crystallinity index of 75% which reduced as time of acetylation increased. Acetylation at mild conditions of 70 °C for 3 h did not result in significant damage to the fibres. For the hardness measurements, the values of the micro-hardness of the composite samples increased from 0.20 GPa to 0.29 GPa. The acetylated fibres tend to further resist deformation due to its dispersion in the polymer matrix providing a stronger cross-linking effect. The properties unearthed show that BF appears to be a promising raw material for the production of polymer composites.

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Reinforcement _recurrent fuzzy rule based system based on brain emotional learning structure to predict the complexity dynamic system

Reinforcement _recurrent fuzzy rule based system based on br...

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International Conference on Digital Information Management (ICDIM)

作者： Mahboobeh Parsapoor Caro Lucas Saeid Setayeshi Islamic Azad University Science and Research Tehran Iran Centre Of Excellence control and intelligent processing ECE facultyCollege of engineering University of Tehran Iran Department of Physics and Nuclear Science Amirkabir University of Technology Tehran Iran

In this study, new approach based on brain emotional Learning process is presented to predict chaotic system more accurate than other learning models. So the main scope of this paper is to reveal the advantages of this learning model that imitate the internal representation of brain emotional learning model to provide a correct response to stimuli to state a purposeful predicting system. The convergence theory is clarified by utilizing the model to predict the complex dynamical Lorenz system. Also the consequence of using this method to forecast such a complex system is compared with obtained results from other related studies that examine other methods such as Locally Linear Model Tree(LOLIMOT) and radial basis function (RBF) Neural network with Orthogonal lest square (OLS ) for predicting the Lorenz chaotic time series. The comparison indicates the superior performance of presented method to make the multi step ahead prediction. Also the effect of noise on the performance of the techniques is also considered. In deed, the learning methods could deal with predicting the future state of complex system with limited training data as well as large data set.

关键词： Fuzzy systems Knowledge based systems Predictive models Brain modeling Chaos Learning systems Biological neural networks Training data Fuzzy sets Process control

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Mobile manipulator tracking control based on observer design

Mobile manipulator tracking control based on observer design

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World Congress on intelligent control and Automation (WCICA)

作者： Yuwei Ren Hong Wang Institute of automation Chinese Academy and Sciences China Research Center of Automation Northeastern University Shenyang China The Control Systems Centre School of Electricaland Electronic Engineering University of Manchester Institute of Science and Technology UK

This paper studies the tracking control problem of mobile manipulators based on state observer. The proposed tracking controller ensures that the full state of the system can asymptotically track the given desired trajectory globally in the presence of the system coupling. Extensive simulations presented in the paper show the effectiveness of the proposed approach.

关键词： Observers Manipulators Automation Mobile communication Couplings Trajectory

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Deep Domain Adaptation Regression for Force Calibration of Optical Tactile Sensors

arXiv

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arXiv 2024年

作者： Chen, Zhuo Ou, Ni Jiang, Jiaqi Luo, Shan Robot Perception Lab Centre for Robotics Research Department of Engineering King’s College London LondonWC2R 2LS United Kingdom State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Optical tactile sensors provide robots with rich force information for robot grasping in unstructured environments. The fast and accurate calibration of three-dimensional contact forces holds significance for new sensors and existing tactile sensors which may have incurred damage or aging. However, the conventional neural-network-based force calibration method necessitates a large volume of force-labeled tactile images to minimize force prediction errors, with the need for accurate Force/Torque measurement tools as well as a time-consuming data collection process. To address this challenge, we propose a novel deep domain-adaptation force calibration method, designed to transfer the force prediction ability from a calibrated optical tactile sensor to uncalibrated ones with various combinations of domain gaps, including marker presence, illumination condition, and elastomer modulus. Experimental results show the effectiveness of the proposed unsupervised force calibration method, with lowest force prediction errors of 0.102N (3.4% in full force range) for normal force, and 0.095N (6.3%) and 0.062N (4.1%) for shear forces along the x-axis and y-axis, respectively. This study presents a promising, general force calibration methodology for optical tactile sensors. Copyright © 2024, The Authors. All rights reserved.

关键词： Forecasting

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TransForce: Transferable Force Prediction for Vision-based Tactile Sensors with Sequential Image Translation

arXiv

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arXiv 2024年

作者： Chen, Zhuo Ou, Ni Zhang, Xuyang Luo, Shan The Robot Perception Lab Centre for Robotics Research Department of Engineering King's College London LondonWC2R 2LS United Kingdom The State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China

Vision-based tactile sensors (VBTSs) provide high-resolution tactile images crucial for robot in-hand manipulation. However, force sensing in VBTSs is underutilized due to the costly and time-intensive process of acquiring paired tactile images and force labels. In this study, we introduce a transferable force prediction model, TransForce, designed to leverage collected image-force paired data for new sensors under varying illumination colors and marker patterns while improving the accuracy of predicted forces, especially in the shear direction. Our model effectively achieves translation of tactile images from the source domain to the target domain, ensuring that the generated tactile images reflect the illumination colors and marker patterns of the new sensors while accurately aligning the elastomer deformation observed in existing sensors, which is beneficial to force prediction of new sensors. As such, a recurrent force prediction model trained with generated sequential tactile images and existing force labels is employed to estimate higher-accuracy forces for new sensors with lowest average errors of 0.69N (5.8% in full work range) in x-axis, 0.70N (5.8%) in y-axis, and 1.11N (6.9%) in z-axis compared with models trained with single images. The experimental results also reveal that pure marker modality is more helpful than the RGB modality in improving the accuracy of force in the shear direction, while the RGB modality show better performance in the normal direction. Copyright © 2024, The Authors. All rights reserved.

关键词： RGB color model

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Marker or Markerless? Mode-Switchable Optical Tactile Sensing for Diverse Robot Tasks

arXiv

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arXiv 2024年

作者： Ou, Ni Chen, Zhuo Luo, Shan The State Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Institute of Technology Beijing100081 China The Robot Perception Lab. Centre for Robotics Research Department of Engineering King’s College London LondonWC2R 2LS United Kingdom

Optical tactile sensors play a pivotal role in robot perception and manipulation tasks. The membrane of these sensors can be painted with markers or remain markerless, enabling them to function in either marker or markerless mode. However, this uni-modal selection means the sensor is only suitable for either manipulation or perception tasks. While markers are vital for manipulation, they can also obstruct the camera, thereby impeding perception. The dilemma of selecting between marker and markerless modes presents a significant obstacle. To address this issue, we propose a novel mode-switchable optical tactile sensing approach that facilitates transitions between the two modes. The marker-to-markerless transition is achieved through a generative model, whereas its inverse transition is realized using a sparsely supervised regressive model. Our approach allows a single-mode optical sensor to operate effectively in both marker and markerless modes without the need for additional hardware, making it well-suited for both perception and manipulation tasks. Extensive experiments validate the effectiveness of our method. For perception tasks, our approach decreases the number of categories that include misclassified samples by 2 and improves contact area segmentation IoU by 3.53%. For manipulation tasks, our method attains a high success rate of 92.59% in slip detection. Code, dataset and demo videos are available at the project website https://***/Marker-Markerless-Transition/ Copyright © 2024, The Authors. All rights reserved.

关键词： Tactile sensors

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A multivariate empirical mode decomposition based filtering for subject independent BCI

A multivariate empirical mode decomposition based filtering ...

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Irish Signals and systems Conference (ISSC)

作者： Pramod Gaur Ram Bilas Pachori Hui Wang Girijesh Prasad Intelligent Systems Research Centre Ulster University L'Derry U.K. Discipline of Electrical Engineering Indian Institute of Technology Indore Indore India School of Computing and Mathematics Ulster University Jordanstown U.K.

ISBN: (纸本)9781509034109

Goal: A brain-computer interface (BCI) provides a way to translate the motion intentions of human using brain signals such as electroencephalogram (EEG) into control commands. EEG signals are highly subject specific and non-stationary. One of the most challenging tasks is to classify motion intentions since the recorded EEG signals have inherent non-stationarities which are due to changes in the signal properties over time within as well as across sessions. Thus it becomes difficult to achieve a stable operation of BCI. Method: We present a novel filtering method based on the multivariate empirical mode decomposition (MEMD) using subject independent BCI (MEMD-SI-BCI) for classification of motor imagery (MI) based EEG signals to achieve enhanced BCI. A subject independent BCI can be used immediately by the new user without using the user's training data. The MEMD method helps to utilize the cross channel information and enhance localization properties. It decomposes multichannel EEG signals into a set of multivariate intrinsic mode functions (MIMFs). These MIMFs can be considered narrow-band, amplitude and frequency modulated (AM-FM) signals. The statistical property, namely, mean frequency measure of these MIMFs has been used to combine these MIMFs to compute the enhanced EEG signals which have major contributions due to μ and β rhythms over the motor cortex region. The objective of the proposed method is to filter EEG signals before feature extraction and classification to enhance the features separability and ultimately the BCI task classification performance. The common spatial pattern (CSP) feature has been computed from the enhanced EEG signals and has been used as a feature set for classification of left hand and right hand MIs using a linear discriminant analysis (LDA) based classification method. Results: We have achieved an improvement of >11% in the evaluation stage using the MEMD-SI-BCI method when compared with SI-BCI. Significance: This study helps to d

关键词： Electroencephalography Feature extraction Empirical mode decomposition Training Linear discriminant analysis Electrooculography Brain-computer interfaces

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Self-Supervised Plant Phenotyping by Combining Domain Adaptation with 3D Plant Model Simulations: Application to Wheat Leaf Counting at Seedling Stage

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Plant Phenomics 2023年第2期5卷 226-238页

作者： Yinglun Li Xiaohai Zhan Shouyang Liu Hao Lu Ruibo Jiang Wei Guo Scott Chapman Yufeng Ge Benoit de Solan Yanfeng Ding Frédéric Baret Plant Phenomics Research Centre Academy for Advanced Interdisciplinary StudiesJiangsu Collaborative Innovation Center for Modern Crop ProductionNanjing Agricultural UniversityNanjingChina Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhanChina Graduate School of Agricultural and Life Sciences The University of Tokyo1-1-1 Midori-choNishitokyo CityTokyoJapan School of Agriculture and Food Sciences The University of QueenslandSt.LuciaQueensland 4072Australia Department of Biological Systems Engineering University of Nebraska-LincolnLincolnNebraska 68583United States INRAE Avignon UniversitéUMR EMMAHUMT CAPTE228route de l’aérodrome-CS 4050984914 Avignon Cedex 9France ARVALIS Institut du Végétal 3 Rue Joseph et Marie Hackin75116 ParisFrance

The number of leaves at a given time is important to characterize plant growth and *** this work,we developed a high-throughput method to count the number of leaves by detecting leaf tips in RGB *** digital plant phenotyping platform was used to simulate a large and diverse dataset of RGB images and corresponding leaf tip labels of wheat plants at seedling stages(150,000 images with over 2 million labels).The realism of the images was then improved using domain adaptation methods before training deep learning *** results demonstrate the efficiency of the proposed method evaluated on a diverse test dataset,collecting measurements from 5 countries obtained under different environments,growth stages,and lighting conditions with different cameras(450 images with over 2,162 labels).Among the 6 combinations of deep learning models and domain adaptation techniques,the Faster-RCNN model with cycle-consistent generative adversarial network adaptation technique provided the best performance(R^(2)=0.94,root mean square error=8.7).Complementary studies show that it is essential to simulate images with sufficient realism(background,leaf texture,and lighting conditions)before applying domain adaptation ***,the spatial resolution should be better than 0.6 mm per pixel to identify leaf *** method is claimed to be self-supervised since no manual labeling is required for model *** self-supervised phenotyping approach developed here offers great potential for addressing a wide range of plant phenotyping *** trained networks are available at https://***/YinglunLi/Wheat-leaf-tip-detection.

关键词： Wheat seedling Plant

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research on intelligent Temperature control Method of Closed-Loop Cooling System Based on Deep Q-Network

SSRN

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SSRN 2024年

作者： Liu, Yongchao Wang, Bo Zheng, Wei Wang, Zibo Li, Tong Cheng, Jiahao Niu, Zhenfeng Ke, Zhiwu Tan, Sichao Li, Jiangkuan Gao, Puzhen Tian, Ruifeng Zhu, Qidan Heilongjiang Provincial Key Laboratory of Nuclear Power System & Equipment Harbin Engineering University Harbin150001 China Key Laboratory of Nuclear Safety and Advanced Nuclear Energy Technology Ministry of Industry and Information Technology Harbin Engineering University Harbin150001 China Science and Technology Thermal Energy and Power Laboratory Wuhan430205 China Wuhan Second Ship Design and Research Institute Wuhan430205 China College of Intelligent Systems Science and Engineering Harbin Engineering University Harbin150001 China

As one of the most important subsystem types in the nuclear power system, the closed-loop cooling system is used for the heat transfer of the system. The control quality of its operating temperature is important for the safe operation of the entire nuclear power system. This paper proposes an intelligent temperature control method based on deep reinforcement learning (DRL). First, the closed-loop cooling system simulator is built and expanded to provide an experimental platform for the implementation of DRL. Secondly, a DRL interaction system is designed based on prior knowledge such as system thermal control characteristics and temperature control targets. Finally, the improved deep Q-network (DQN) algorithm is used to train the deep neural network agent, and the optimal temperature control strategy is approached and saved through self-learning interaction. The experimental results demonstrate that the DRL controller can realize automatic control operation and has good target tracking ability and robustness. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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Improving Incremental Encoder Measurement: Variable Acquisition Window and Quadrature Phase Compensation to Minimize Acquisition Errors

Improving Incremental Encoder Measurement: Variable Acquisit...

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IEEE International Conference on Autonomous Robot systems and Competitions (ICARSC)

作者： José Lima Ví tor H. Pinto A. Paulo Moreira Paulo Costa Research Centre in Digitalization and Intelligent Robotics (CeDRI) Instituto Polit&#x00B4 ecnico de Braganca Campus de Santa Apolonia Braganca Portugal INESC Technology and Science Porto Portugal SYSTEC (DIGI2) - Research Center for Systems and Technologies (Digital and Intelligent Industry Lab) Porto Portugal Faculty of Engineering of University of Porto Portugal

Motion control is an important task in several areas, such as robotics where the angular position and speed should be acquired, usually with encoders. For slow angular speeds, an error is introduced spoiling the measurement. In this paper there will be proposed two new methodologies, that when combined allow to increase the precision whereas reducing the error, even on transient velocities. The two methodologies Variable Acquisition Window and a Quadrature Phase Compensation are addressed and combined simultaneously. A real implementation of the proposed algorithms is performed on a real hardware, with a DC motor and a low resolution encoder based on hall effect. The results validate the proposed approach since the errors are reduced compared with the standard Quadrature Encoder Reading.

关键词： Phase measurement Conferences Measurement uncertainty DC motors Hardware Hall effect Transient analysis

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