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共 1478 条记录，以下是151-160 订阅

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Algorithm for Robustness Estimation of Large Deviations in Linear Discrete-Time Systems 17th

Algorithm for Robustness Estimation of Large Deviations in...

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17th international conference on informatics in control, automation and robotics, ICINCO 2020

作者： Vunder, Nina Dudarenko, Natalia Institute of Control Engineering TU Braunschweig Hans-Sommer-Str. 66 Braunschweig Germany Department of Control Systems and Industrial Robotics ITMO University 49 Kronverksky Pr. Saint-Petersburg Russia

ISBN: (纸本)9783030924416

The paper presents the algorithm for robustness estimation of large deviations in free motion of linear discrete-time systems. A tracking discrete-time systems with the modal control law are discussed, where the modal control law is designed taking into account the value of delay and the deviation. Two cases of discrete-time systems with control signal delay are considered in the paper. The first case relates to predictable delay of control signal, and the second one relates to unpredictable delay of control signal. It is assumed that parameters of the system are linearly dependent on the uncertainties. The problem is solved with the state space approach and the sensitivity theory methods. An upper bound estimation of trajectory deviations for discrete-time systems is obtained. The estimation contains the condition number of the eigenvectors matrix of the system state matrix. Therefore, sensitivity functions of singular values of the eigenvectors matrix are used to calculate the robustness estimation of the deviations. Based on the obtained equations, an algorithm for the robustness estimation of large deviations in linear discrete-time systems with parametric uncertainties is proposed. The proposed method is compared with an alternative LMI based approach for the peak estimation problem. Advantages and disadvantages of both approaches are illustrated with examples. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Sensitivity analysis

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Sentiment Analysis on the Use of Electric Vehicles With Long Short-Term Memory

Sentiment Analysis on the Use of Electric Vehicles With Long...

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control & automation, Electronics, robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE international conference on

作者： Rizki Elisa Nalawati Dewi Yanti Liliana Ratna Widya Iswara Izzati Rozana Putri Dzakiyah Fauziyah Dept. of Informatics and Computer Engineering Politeknik Negeri Jakarta Depok Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

Electric vehicles, or EVs, have drawn a lot of attention lately as an eco-friendly way to cut carbon emissions and lessen reliance on fossil fuels. This work uses a neural network model called Long Short-Term Memory (LSTM), which is effective at processing sequential input, to analyze user attitudes toward electric vehicles. Opinion data were gathered from the social media network Twitter between March 1st and March 31st, 2023, using the keyword “electric vehicles”. Based on the obtained opinion data, the LSTM approach is utilized to categorize sentiments as positive or negative. Implementation of the random oversampling technique is important to be applied in increasing the accuracy of the $\mathbf{n}$ recall model for the specified class. The study's conclusions show that the LSTM model performs admirably when it comes to sentiment analysis using the test dataset. The model evaluation yields a precision of $\mathbf{99.65 \%}$ and an accuracy rating of $\mathbf{97.79 \%}$ . The modeling process is carried out with variance splitting data where the highest accuracy is obtained from a comparison of training testing data of $\mathbf{90 \%}$ : $\mathbf{10 \%}$ .

关键词： Training Sentiment analysis Analytical models Accuracy Social networking (online) Blogs Electric vehicles Data models Long short term memory Testing

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Sentiment Analysis Twitter (X) Users Towards the Boycott of Pro-Israel Brand Using Support Vector Machine Method with SMOTE Approach

Sentiment Analysis Twitter (X) Users Towards the Boycott of ...

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control & automation, Electronics, robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE international conference on

作者： Hana Patricia Mawarnie Widodo Murien Nugraheni Dept. of Informatics and Computer Engineering Education Universitas Negeri Jakarta Jakarta Indonesia Dept. of Information System and Technology Universitas Negeri Jakarta Jakarta Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

The Israel-Palestine conflict that occurred on October 7, 2023, resulted in a boycott of brands known to be pro-Israel. Twitter or X, as one of the largest social media platforms in the world, became a platform for people to voice their opinions regarding the boycott. This study aims to analyze the sentiment of Twitter or X users towards the boycott of pro-Israel brands using the Support Vector Machine (SVM) method with the SMOTE (Synthetic Minority Oversampling Technique) approach. The Support Vector Machine (SVM) method is a machine learning method used for classification and regression. SMOTE is a technique used to overcome data imbalance. The data used in this study is Indonesian language tweet data from October 7, 2023, to April 7, 2024. The data was collected using data crawling techniques. The results of the study from the 80:20 data split, show that the Support Vector Machine (SVM) polynomial kernel model with the SMOTE technique has better performance in analyzing the sentiment of Twitter or X users towards the boycott of pro-Israel brands, with an accuracy value of 98,47%.

关键词： Support vector machines Sentiment analysis Accuracy Social networking (online) Blogs Machine learning Polynomials Internet of Things Kernel Robots

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Towards independent SMA actuators: A novel analog two-state control system for flexible robots

Towards independent SMA actuators: A novel analog two-state ...

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international conference on Emerging Technologies and Factory automation (ETFA)

作者： Florian-Alexandru Brasoveanu Adrian Burlacu Department of Automatic Control and Applied Informatics "Gheorghe Asachi" Technical University of Iasi Iasi Romania

The soft robotics field offers a large variety of actuators and robots, complex structures and designs able to behave in a required way, offering speed and resistance and at the same time reducing the contact damage chance. The control of such structures using mathematical methods requires complex methods which evolved over time in the form of predictive control and even neural networks and genetic algorithms. The current paper presents ongoing research that can be found at the intersection of meta-materials and sensor technology. It approaches a simpler method of controlling a flexible actuator based on a shape memory alloy, the regulator being a two-state switch controller steered by a pressure force sensor. We propose a complete analogical electrical structure and analyze the performance of the outcome, focusing on time response. The present work also contains a brief description of a mechanical design proposal able to optimize the impact of the shape memory alloy used, in order to obtain an amplified actuation.

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Enhancing Extrusion Precision and Reliability in Robotic Multi-Axis Additive Manufacturing through Digital Twin Integration

Enhancing Extrusion Precision and Reliability in Robotic Mul...

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international conference on control, automation and Systems ( ICCAS)

作者： Tomáš Jochman Václav Voltr Ondřej Švec Václav Kubáček Pavel Burget Václav Hlaváč Department of Cybernetics Faculty of Electrical Engineering Czech Technical University in Prague Prague Czech Republic Czech Institute of Informatics Robotics and Cybernetics Czech Technical University in Prague Prague Czech Republic

ISBN: (数字)9788993215380

ISBN: (纸本)9798331517939

In robotic multi-axis additive manufacturing, achieving high precision and consistency is crucial. This paper presents a comprehensive calibration procedure and optimization strategy for process parameters in a filament-based extruder mounted on an industrial robot. A digital twin of the manufacturing setup is utilized to simulate and validate the process, enabling precise adjustments and automation of code generation for robotic operations. Experimental calibration artifacts are produced and measured to verify the effectiveness of the proposed approach, highlighting the impact of variables such as hot-end temperature, layer height, and tool center point velocity on print quality. A laser tracker is employed to ensure precise measurement of calibration artifacts, enhancing the accuracy of the calibration process. The results highlight the potential for advancements in the precision and reliability of multi-axis additive manufacturing processes through systematic calibration and optimization, enabling the stable production of complex 3D-printed structures and reduced production waste.

关键词： Temperature measurement Accuracy Systematics Service robots Production Three-dimensional printing Calibration Manufacturing Robots Optimization

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ICINCO 2019 - Proceedings of the 16th international conference on informatics in control, automation and robotics

ICINCO 2019 - Proceedings of the 16th International Conferen...

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16th international conference on informatics in control, automation and robotics, ICINCO 2019

ISBN: (纸本)9789897583803

The proceedings contain 170 papers. The topics discussed include: evolving systems and their automotive applications;fault training matrix for process monitoring based on structured residuals;a PGD-based method for robot global path planning: a primer;generation of complex data for ai-based predictive maintenance research with a physical factory model;exploiting physical contacts for robustness improvement of a dot-painting mission by a micro air vehicle;on some open-ended challenges in model-based fault management for aerospace systems: a look backwards and forwards;camera and lidar cooperation for 3D feature extraction;balancing control of a self-driving bicycle;and singularity analysis for redundant manipulators of arbitrary kinematic structure.

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ICINCO 2019 - Proceedings of the 16th international conference on informatics in control, automation and robotics

ICINCO 2019 - Proceedings of the 16th International Conferen...

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16th international conference on informatics in control, automation and robotics, ICINCO 2019

ISBN: (纸本)9789897583803

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Carrying the uncarriable: a deformation-agnostic and human-cooperative framework for unwieldy objects using multiple robots

Carrying the uncarriable: a deformation-agnostic and human-c...

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IEEE international conference on robotics and automation (ICRA)

作者： Doganay Sirintuna Idil Ozdamar Arash Ajoudani Human-Robot Interfaces and Interaction Laboratory Istituto Italiano di Tecnologia Genoa Italy Dept. of Informatics Bioengineering Robotics and System Engineering University of Genoa Genoa Italy

This manuscript introduces an object deformability-agnostic framework for co-carrying tasks that are shared between a person and multiple robots. Our approach allows the full control of the co-carrying trajectories by the person while sharing the load with multiple robots depending on the size and the weight of the object. This is achieved by merging the haptic information transferred through the object and the human motion information obtained from a motion capture system. One important advantage of the framework is that no strict internal communication is required between the robots, regardless of the object size and deformation characteristics. We validate the framework with two challenging real-world scenarios: co-transportation of a wooden rigid closet and a bulky box on top of forklift moving straps, with the latter characterizing deformable objects. In order to evaluate the generalizability of the proposed framework, a heterogenous team of two mobile manipulators that consist of an Omni-directional mobile base and a collaborative robotic arm with different DoFs is chosen for the experiments. The qualitative comparison between our controller and the baseline controller (i.e., an admittance controller) during these experiments demonstrated the effectiveness of the proposed framework especially when co-carrying deformable objects. Furthermore, we believe that the performance of our framework during the experiment with the lifting straps offers a promising solution for the co-transportation of bulky and ungraspable objects.

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Causal Inference for De-biasing Motion Estimation from Robotic Observational Data

Causal Inference for De-biasing Motion Estimation from Robot...

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IEEE international conference on robotics and automation (ICRA)

作者： Junhong Xu Kai Yin Jason M. Gregory Lantao Liu Luddy School of Informatics Computing and Engineering at Indiana University Bloomington IN USA Expedia Group. U.S. Army Research Laboratory Adelphi MD

Robot data collected in complex real-world scenarios are often biased due to safety concerns, human preferences, and mission or platform constraints. Consequently, robot learning from such observational data poses great challenges for accurate parameter estimation. We propose a principled causal inference framework for robots to learn the parameters of a stochastic motion model using observational data. Specifically, we leverage the de-biasing functionality of the potential-outcome causal inference framework, the Inverse Propensity Weighting (IPW), and the Doubly Robust (DR) methods, to obtain a better parameter estimation of the robot's stochastic motion model. The IPW is a re-weighting approach to ensure unbiased estimation, and the DR approach further combines any two estimators to strengthen the unbiased result even if one of these estimators is biased. We then develop an approximate policy iteration algorithm using the bias-eliminated estimated state transition function. We validate our framework using both simulation and real-world experiments, and the results have revealed that the proposed causal inference-based navigation and control framework can correctly and efficiently learn the parameters from biased observational data.

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Evaluating Contemporary Machine Learning and Deep Learning Strategies for Intrusion Detection

Evaluating Contemporary Machine Learning and Deep Learning S...

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control & automation, Electronics, robotics, Internet of Things, and Artificial Intelligence (CERIA), IEEE international conference on

作者： Riza Sauqi Valasev Anas Rachmadi Priambodo Ratih Nur Esti Anggraini Department of Informatics Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798331511166

ISBN: (纸本)9798331511173

As cyber threats evolve with increasing complexity, the imperative for advanced Network Intrusion Detection Systems (NIDS) becomes paramount. This study offers a rigorous comparative analysis of traditional Machine Learning (ML) algorithms and cutting-edge Deep Learning (DL) techniques within the framework of the contemporary CICIDS-2018 dataset, aimed at evaluating their proficiency in network intrusion detection. The machine learning classifiers used are K-Nearest Neighbors, Logistic Regression, Random Forest, and Support Vector Machines, against a hybrid DL model combining Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU). The study reveals that while ML algorithms maintain the benefit of interpretability and lower computational demands, the hybrid CNN-GRU model showcases exceptional capability in discerning intricate and latent patterns indicative of intrusive activities, hence yielding superior detection accuracy and demonstrating resilience against overfitting. The complex dynamics of network security require adaptive model selection, and this comparison research shows the efficacy of merging ML and DL approaches. The results affirm the viability of deploying a hybrid analytical framework that capitalizes on the strengths of both ML and DL, leveraging the comprehensive and diverse nature of the CICIDS-2018 dataset to bolster the security posture of digital infrastructures against the burgeoning wave of cyber threats.

关键词： Deep learning Support vector machines Adaptation models Machine learning algorithms Computational modeling Network intrusion detection Classification algorithms Convolutional neural networks Robots Resilience

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