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computational intelligence on Engineering/Cyber Physical Systems (CIES), ieee symposium on

作者： Ozan Kaya Seniz Ertugrul Masoud Abedinifar Olav Egeland Dept. of Mechanical and Industrial Eng. Norwegian University of Science and Technology (NTNU) Trondheim Norway Dept. of Mechatronics Engineering Izmir University of Economics (IUE) Izmir Turkey Dept. of Neurology University Hospital Schleswig-Holstein (USKH) Kiel Germany Dept of Mechanical and Industrial Engineering Norwegian University of Science and Technology (NTNU) Trondheim Norway

ISBN: (数字)9798331508272

ISBN: (纸本)9798331508289

Different friction forces or torques are affecting the system's performance and control. Friction forces occur due to bearings, gearboxes, or any other contacts in the system. Researchers have reported different forms of friction, such as stiction, viscous and Stribeck effects, pre-sliding displacement, stick-slip effects, hysteresis (or frictional lag), etc. Developing a mathematical model to describe the underlying dynamics of a complex system may become necessary to design either a modelbased controller or at least compensate for the non-linear effects of friction forces. For this reason, either test set-ups or datadriven techniques might be used. In this study, the RecurrentPhysics Informed Neural Network is studied to determine the friction forces and model the Ball and beam system. While PINN provides faster results to model non-linear systems with noisy and small data sizes, Recurrent Neural Network architecture is fruitful for modeling time-dependent systems. Thus, R-PINN is trained with noisy signals for system response and friction model of the ball and beam system. Despite noisy signals and nonlinearity in the system, R-PINN is promising in modeling the system response and estimating the friction model.

关键词： Recurrent neural networks Friction computational modeling System performance Dynamics Training data Predictive models Data models Mathematical models Noise measurement

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Estimation of External Force acting on Underwater Robots

Estimation of External Force acting on Underwater Robots

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ieee International Conference on automation Science and Engineering (CASE)

作者： Srinivasan Lakshminarayanan Daniel Duecker Andriy Sarabakha Amartya Ganguly Leila Takayama Sami Haddadin Munich Institute of Robotics and Machine Intelligence (MIRMI) TU Munich Germany Department of Electrical and Computer Engineering Aarhus University Denmark Department of Computational Media University of California USA

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

In the context of robotics, accurately estimating external forces is essential. These estimations play a crucial role in collision detection, motion planning, robot control, disturbance compensation, environmental interactions, etc. Given the importance of external force estimation, We introduce a framework to estimate the forces acting on underwater robots. By leveraging dynamic models of the underwater remotely operated vehicle (ROV), our framework predicts and estimates forces based on velocity and acceleration data. We conducted real-world experiments on BlueROV2 underwater vehicle to test and verify the efficacy of our proposed framework. We determined that our framework can predict the external forces with an MAE of 0.8 N.

关键词： Underwater structures Autonomous underwater vehicles Force measurement Remotely guided vehicles Force Robot control Estimation Mathematical models Vehicle dynamics Collision avoidance

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Examining Machine Learning for 5G and Beyond Through an Adversarial Lens

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ieee INTERNET COMPUTING 2021年第2期25卷 26-34页

作者： Usama, Muhammad Ilahi, Inaam Qadir, Junaid Mitra, Rupendra Nath Marina, Mahesh K. Informat Technol Univ Lahore 54000 Pakistan Univ Edinburgh Edinburgh EH8 9YL Midlothian Scotland

Spurred by the recent advances in deep learning to harness rich information hidden in large volumes of data and to tackle problems that are hard to model/solve (e.g., resource allocation problems), there is currently tremendous excitement in the mobile networks domain around the transformative potential of data-driven artificial intelligence/machine learning (AI/ML) based network automation, control and analytics for 5G and beyond. In this article, we present a cautionary perspective on the use of AI/ML in the 5G context by highlighting the adversarial dimension spanning multiple types of ML (supervised/unsupervised/reinforcement learning) and support this through three case studies. We also discuss approaches to mitigate this adversarial ML risk, offer guidelines for evaluating the robustness of ML models, and call attention to issues surrounding ML oriented research in 5G more generally.

关键词： 5G mobile communication Modulation computational modeling Security Context modeling Cloud computing Signal to noise ratio 5G and Beyond Mobile Networks Adversarial Machine Learning Security I

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Design of collaborative computing mining truck automatic driving system based on AUTOSAR Adaptive Platform

Design of collaborative computing mining truck automatic dri...

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2022 International Conference on Computer Engineering and Artificial intelligence, ICCEAI 2022

作者： Su, Weixing University, Tiangong Xu, Shengjie Liu, Fang School of Computer Science and Technology Tiangong University Tianjin China Tianjin China School of Artificial Intelligence

ISBN: (数字)9781665468039

ISBN: (纸本)9781665468039

The high complexity and high computational resource requirements of the autonomous driving system require it to be designed not only to meet requirements of stringent integrity and security, but also to be iteratively updated over the life cycle of the vehicle as external systems evolve. In addition, the heavy mining trucks are severely bumped on the rough mining road, making the expensive lidar on the mining truck very vulnerable to damage, and if all sensors are deployed on the mining truck, there will also be a problem of excessive processing unit load. Aiming at the above problems, a design scheme of cooperative computing mining truck automatic driving system based on AUTOSAR Adaptive Platform is proposed. The design scheme includes a cloud control and management system, a vehicle control and management system and a road detection and identification system. The three-in-one, coordinated operation not only meets the needs of the automation and intelligence of the mining truck autonomous driving system, as well as unified management, operation and maintenance, but also supports high performance and collaborative computing, secure and reliable communication mechanisms and flexible software configuration capabilities. © 2022 ieee.

关键词： Roads and streets

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A Receding Horizon-type Solution of the Inverse Kinematic Task of Redundant Robots 15

A Receding Horizon-type Solution of the Inverse Kinematic Ta...

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ieee 15th International symposium on Applied computational intelligence and Informatics (SACI)

作者： Issa, Hazem Varga, Bence Tar, Jozsef K. Obuda Univ Doctoral Sch Appl Informat & Appl Math Budapest Hungary Obuda Univ Inst Mechatron & Vehicle Engn Donat Banki Fac Mech & Safety Engn Budapest Hungary Obuda Univ Antal Bejczy Ctr Intelligent Robot Univ Res & Innovat Ctr Budapest Hungary Obuda Univ John von Neumann Fac Informat Inst Appl Math Budapest Hungary

ISBN: (纸本)9781728195438

With the exception of specially designed kinematic structures, the inverse kinematic task of redundant robots cannot be solved in "closed form". Redundancy is often needed to make the arm "dexterous". In general numerical techniques have to be applied. A typical approach is finding the "differential solution" by formulating the Jacobian of the problem in the first step, and using some generalized inverse that selects a definite solution of the ambiguous set of the possible ones. Each of these solutions suffers from huge joint coordinate time-derivatives in the vicinity of the kinematic singularities that are evaded by "deforming" the original task. A quasi-differential approach was recently elaborated that, though calculates the Jacobian, instead "inverting" it, applies a fixed point iteration that automatically evades the singularities. However, its solution depends on the eigenvalues of the Jacobian, and it was found not "flexible enough" for all practical purposes. The Moore-Penrose solution can be generalized as an optimization task under constraints for quite complex cost functions. The hard constraint in this approach forces the exact solution of the inverse kinematic task when it is possible. Due to it the intent of "minimizing the costs" is "overridden" in the vicinity of the singularities. In the present paper, based on the formal structure of the Receding Horizon controllers, an alternative solution is suggested that allows flexibility for relaxing the costs and the "hard constraints", too. Its computational complexity to some extent is reduced via evading the technique of gradient reduction. Simulation results are presented for a 7 DoF robot arm.

关键词： Inverse Kinematic Task Gradient Descent Method Reduced Gradient Method Moore-Penrose Pseudoinverse Gram-Schmidt Algorithm Receding Horizon control

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Quadratic Regression Model Based Predictive control of PMSM Drives with Field Weakening Operation Capability Constrained to the Linear Modulation Range

Quadratic Regression Model Based Predictive Control of PMSM ...

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ieee International symposium on Industrial Electronics (ISIE)

作者： Kristóf Bándy Péter Stumpf Department of Automation and Applied Informatics Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Műegyetem rkp. 3. H-1111 Budapest Hungary

Continuous control Set Model Predictive control in power electronics has prospective outlooks, however, due to the computational requirements, these techniques are still underutilized. In the paper, a novel method is discussed that allows the reduction of the complex optimization underlying the predictive control algorithm. However, the optimization is further restricted with a non-linear (circular) constraint, that allows the modulator to remain in the linear modulation region. The paper offers an effective solution to the optimization problem, with simulation results showing the effectiveness of the proposed method.

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Comparison of Automatic Question Generation Techniques

Comparison of Automatic Question Generation Techniques

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International symposium on computational intelligence and Informatics

作者： Walelign Tewabe Sewunetie László Kovács Institute of Informatics University of Miskolc Miskolc Hungary

ISBN: (纸本)9798350398830

Automatic question generation is a technique that generates a question from various sources like structured or unstructured content. Even if, each method has its own shortcoming and strength, we didn’t find any study that did an experimental comparison of the current automatic question generation techniques using a similar dataset. In this study, we analyzed the different state-of-the-art research works, and we identified significant challenges of each model. In addition, we have used BLEU, METEOR, and ROUGE automatic evaluation metrics, to evaluate the experimental test result. From our evaluation result, we observed that most of the tested techniques score below 0.5 in all automated evaluation metrics. Out of tested techniques T5-transformer-based, scores the maximum result. This result point out this research area still needs further investigation and preparing standardized training.

关键词： Measurement Training Computer science Analytical models Mechatronics automation Informatics

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Mobile Robot Navigation Using ORB-SLAM3: A Multi-Modal Autonomous System

Mobile Robot Navigation Using ORB-SLAM3: A Multi-Modal Auton...

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2025 International Conference on Electrical automation and Artificial intelligence, ICEAAI 2025

作者： Da Conceicao, Ariano Tian, Lianfang Du, Qiliang Yuan, Ling School of Automation Science and Engineering South China University of Technology Guangzhou510641 China Research Institute of Modern Industrial Innovation South China University of Technology Guangzhou China Guangdong Engineering Research Center of Cloud-Edge-End Collaboration Technology for Smart City Guangzhou China Key Laboratory of Autonomous Systems and Network Control of Ministry of Education Guangzhou China Guangzhou China Sino-Singapore International Joint Research Institute Guangzhou511363 China

ISBN: (纸本)9798331506797

Indoor mobile robots require reliable solutions for mapping, localization, and navigation tasks. This paper presents a mobile robot system that implements the ability to realize mapping using visual SLAM with ORB-SLAM3 and LiDAR-based mapping with Gmapping for indoor perception and navigation through a modular ROS architecture. The framework also implements autonomous navigation and an autonomous Rapidly-exploring Random Trees (RRT) exploration module with adaptive reset mechanisms. The system operates on a ROS-based platform equipped with an Intel RealSense D435i RGB-D camera and LDS-02 LiDAR scanner. Experimental validation in simulated indoor environments demonstrates the system's capabilities and the presented architecture achieves computational efficiency through selective utilization of mapping modules while maintaining mapping accuracy through complementary sensor modalities. © 2025 ieee.

关键词： LiDAR SLAM Mapping Navigation ORBSLAM 3 ROS RRT

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ChronoProf: Profiling Time Series Forecasters and Classifiers in Mobile Networks with Explainable AI

ChronoProf: Profiling Time Series Forecasters and Classifier...

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ieee International symposium on World of Wireless Mobile and Multimedia Networks (WoWMoM)

作者： Pablo Fernández Pérez Iñaki Bravo Anirudh Kamath Claudio Fiandrino Joerg Widmer IMDEA Networks Institute Spain Universidad Carlos III de Madrid Spain

ISBN: (数字)9798331538323

ISBN: (纸本)9798331538330

The next-generation of mobile networks will increasingly rely on Artificial intelligence (AI)/Machine Learning (ML) for effective network automation, resource orchestration and management. This translates into performing classification and regression tasks on time series data. Unfortunately, the existing AI/ML models are inherently complex and hard to interpret, which hinders their deployment in production networks. Further, the vast majority of the existing EXplainable Artificial intelligence (XAI) techniques are either primarily conceived for computer vision and natural language processing and thus fail to provide useful *** this paper, we take the research on XAI for time series classification and regression tasks one step further proposing ChronoProf, a new tool that builds on legacy XAI techniques. By creating a linearized version of the original model for different observations, ChronoProf provides insights about the dynamic changes in the model decision-making process across observations and is agnostic to the influence of feature magnitude, which is a key limitation of legacy explainers. Thus, ChronoProf highlights the real influence of model parameters on the output. Our extensive evaluation with real-world mobile traffic traces shows that ChronoProf is able to measure the feature importance, especially in classification tasks where linearized explanations across observations show high consistency.

关键词： Wireless communication Translation Explainable AI computational modeling Time series analysis Linear approximation Production Predictive models Natural language processing Next generation networking

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An Expansion-based Document Ranking Framework Incorporated with Core Concern Capturing 5

An Expansion-based Document Ranking Framework Incorporated w...

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5th International Conference on Intelligent Autonomous Systems, ICoIAS 2022

作者： Qi, Yinqiao Xin, Jinghao Li, Ning Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Engineering Research Center of Intelligent Control and Management Department of Automation Shanghai China

ISBN: (数字)9781665498388

ISBN: (纸本)9781665498388

Entity-based ranking is a popular research area in document ranking as knowledge graphs can introduce additional evidence for ranking systems. In most entity-based ranking framework, the original query is transformed into a set of entities and candidate documents are ranked according to their relevance to the entity set. However, mistakes can easily occur during the transformation process, which introduce noise to the ranking system. This paper represents an expansion-based ranking framework, which expand the original query with high-quality candidate entities to mitigate the impact of transformation mistakes and enrich the semantics of the query in the meanwhile. Compared with existing query expansion methods, our expansion method is lightweight as it reduces much computational burden and requests little extra information. However, the core concerns of the users might be covered up after query expansion. To alleviate this dilemma, a core concern capturing method is pro-posed, wherein the attention weight of each inter-entity relation is measured to capture the core entity pairs in the expanded query. Experiments on the S2-CS dataset demonstrate the effectiveness of our lightweight query expansion method and the superiority of our core concern capturing method. Compared with a classical retrieval model, our method provides 11% improvements in terms of NDCG@15. © 2022 ieee.

关键词： Semantics

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