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International Conference on Intelligent Transportation

作者： Yongqi Dong Chang Liu Yiyun Wang Zhe Fu Delft University of Technology Delft The Netherlands RWTH Aachen University Aachen Germany Department of Networked Systems and Services Budapest University of Technology and Economics Budapest Hungary Machine Perception Research Laboratory of HUN-REN Institute for Computer Science and Control (SZTAKI) Budapest Hungary University of California Berkeley CA United States

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Recognizing and understanding implicit driving cues across diverse cultures is imperative for fostering safe and efficient global transportation systems, particularly when training new immigrants holding driving licenses from culturally disparate countries. Additionally, it is essential to consider cross-cultural differences in the development of Automated Driving features tailored to different countries. Previous piloting studies have compared and analyzed cross-cultural differences in selected implicit driving cues, but they typically examine only limited countries. However, a comprehensive worldwide comparison and analysis are lacking. This study conducts a thorough review of existing literature, online blogs, and expert insights from diverse countries to investigate cross-cultural disparities in driving behaviors, specifically focusing on implicit cues such as non-verbal communication (e.g., hand gestures, signal lighting, honking), norms, and social expectations. Through comparative analysis, variations in driving cues are illuminated across different cultural contexts. Based on the findings and identified gaps, a research roadmap is proposed for future research to further explore and address these differences, aiming to enhance intercultural communication, improve road safety, and increase transportation efficiency on a global scale. This paper presents the pioneering work towards a comprehensive understanding of the implicit driving cues across cultures. Moreover, this understanding will inform the development of automated driving systems tailored to different countries considering cross-cultural differences.

关键词： Hands Training Reviews Velocity control Transportation Lighting Road safety Cultural differences Global communication Driver behavior

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Ranking of attributes—comparative study based on data from stylometric domain

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Procedia computer Science 2022年 207卷 2737-2746页

作者： Beata Zielosko Urszula Stańczyk Krzysztof Żabiński Institute of Computer Science University of Silesia in Katowice Będzińska 39 41-200 Sosnowiec Poland Department of Graphics Computer Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 2A 44-100 Gliwice Poland

The area of feature selection methods constantly expands along with the development of artificial intelligence domain, and has great impact on almost every field, whenever data is processed and explored. The paper presents research where a ranking method was proposed, inspired by an approach which comes from an algorithm for induction of decision rules. The ranking procedure was based on calculation of standard deviation for attributes, taking into account assigned class labels. This method was compared with another ranking mechanism, a modified version of popular Relief algorithm, with incorporating characteristics of variables by supervised discretisation. Comparison of obtained results included the aspect of knowledge representation as well as the perspective of the accuracy for constructed rule-based classifiers. The experiments were performed on datasets from stylometry domain, where authorship attribution was considered as a classification task, and stylometric descriptors as characteristic features defining writing styles of authors.

关键词： Feature selection Ranking of attributes Relief Standard deviation Decision rules Stylometry Supervised discretisation Authorship attribution

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Frequency response data based LPV controller synthesis applied to a control moment gyroscope

arXiv

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arXiv 2021年

作者： Bloemers, Tom Tóth, Roland Oomen, Tom The Control Systems Group Department of Electrical Engineering Eindhoven University of Technology Control Systems Technology Department of Mechanical Engineering Eindhoven University of Technology The Systems and Control Laboratory Institute for Computer Science and Control Kende u. 13-17 BudapestH-1111 Hungary

control of systems with operating condition-dependent dynamics, including control moment gyroscopes, often requires operating condition-dependent controllers to achieve high control performance. The aim of this paper is to develop a frequency response data-driven linear parameter-varying control design approach for single-input single-output systems, which allows improved performance for a control moment gyroscope. A stability theory using closed-loop frequency response function data is developed, which is subsequently used in a synthesis procedure that guarantees local stability and performance. Experimental results on a control moment gyroscope demonstrate the performance improvements. Copyright © 2021, The Authors. All rights reserved.

关键词： controllers

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Investigation of Automatic Part-of-Speech Tagging using CRF, HMM and LSTM on Misspelled and Edited Texts 5

Investigation of Automatic Part-of-Speech Tagging using CRF,...

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5th Artificial Intelligence and Cloud Computing Conference, AICCC 2022

作者： Aydinov, Farhad Huseynov, Igbal Sayadzada, Sofiya Rustamov, Samir Data Science University of Glasgow United Kingdom Computational Linguistics and Language Technologies Charles University Prague Czech Republic Department of Applied Mathematics and Computer Science Technical University of Denmark Copenhagen Denmark School of Information Technology and Engineering ADA University Baku Azerbaijan Institute of Control Systems BakuVarna Bulgaria Azerbaijan

ISBN: (纸本)9781450398749

Part-of-speech tagging is the process of assigning words in a given text to appropriate parts-of speech in order to reduce the disambiguation which may arise depending on the contextual usage of the words. In this paper, the problem of word sense disambiguation in Azerbaijani language is addressed by applying part of speech tagging on two varying data corpora, misspelled, and edited (clean) text using 3 different machine learning algorithms: Hidden Markov Model, Long Short-Term Memory, and Conditional Random Fields. The comparative analysis on the outcomes of the mentioned algorithms and their accuracy scores were analysed in the paper. The misspelled dataset for the experiments is provided by Unibank from their chatbot dialogues while the clean textual data was retrieved from the books and newspapers in Azerbaijani. The experiments showed that the Bidirectional LSTM has the highest accuracy scores for both edited (98.2%) and noisy (96.2%) data corpora. Suggested models can be used in the application of algorithms focuses on part of speech tags and syntactic structure of Azerbaijani language which is an agglutinative language belonging to Turkic languages family, thus enabling the research to be further investigated in other agglutinative languages with similar grammatical structure. © 2022 ACM.

关键词： Hidden Markov models

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Data-driven Force Observer for Human-Robot Interaction with Series Elastic Actuators using Gaussian Processes

Data-driven Force Observer for Human-Robot Interaction with ...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Samuel Tesfazgi Markus Keßler Emilio Trigili Armin Lederer Sandra Hirche Chair of Information-Oriented Control (ITR) TUM School of Computation Information and Technology Technical University of Munich Munich Germany Scuola Superiore Sant’Anna BioRobotics Institute Pontedera Italy Department of Computer Science Learning & Adaptive Systems Group ETH Zurich Zurich Switzerland

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Ensuring safety and adapting to the user’s behavior are of paramount importance in physical human-robot interaction. Thus, incorporating elastic actuators in the robot’s mechanical design has become popular, since it offers intrinsic compliance and additionally provide a coarse estimate for the interaction force by measuring the deformation of the elastic components. While observer-based methods have been shown to improve these estimates, they rely on accurate models of the system, which are challenging to obtain in complex operating environments. In this work, we overcome this issue by learning the unknown dynamics components using Gaussian process (GP) regression. By employing the learned model in a Bayesian filtering framework, we improve the estimation accuracy and additionally obtain an observer that explicitly considers local model uncertainty in the confidence measure of the state estimate. Furthermore, we derive guaranteed estimation error bounds, thus, facilitating the use in safety-critical applications. We demonstrate the effectiveness of the proposed approach experimentally in a human-exoskeleton interaction scenario.

关键词： Estimation error Actuators Uncertainty Force measurement Accuracy Measurement uncertainty Force Human-robot interaction Gaussian processes Observers

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Asymmetric Feedback Learning in Online Convex Games

arXiv

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arXiv 2023年

作者： Wang, Zifan Yi, Xinlei Shen, Yi Zavlanos, Michael M. Johansson, Karl H. Division of Decision and Control Systems School of Electrical Enginnering and Computer Science Kth Royal Institute of Technology Sweden Digital Futures StockholmSE-10044 Sweden The Lab for Information & Decision Systems Massachusetts Institute of Technology United States The Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States

This paper considers online convex games involving multiple agents that aim to minimize their own cost functions using locally available feedback. A common assumption in the study of such games is that the agents are symmetric, meaning that they have access to the same type of information or feedback. Here we lift this assumption, which is often violated in practice, and instead consider asymmetric agents;specifically, we assume some agents have access to first-order gradient feedback and others have access to the zeroth-order oracles (cost function evaluations). We propose an asymmetric feedback learning algorithm that combines the agent feedback mechanisms. We analyze the regret and Nash equilibrium convergence of this algorithm for convex games and strongly monotone games, respectively. Specifically, we show that our algorithm always performs between pure first-order and zeroth-order methods, and can match the performance of these two extremes by adjusting the number of agents with access to zeroth-order oracles. Therefore, our algorithm incorporates the pure first-order and zeroth-order methods as special cases. We provide numerical experiments on an online market problem for both deterministic and risk-averse games to demonstrate the performance of the proposed algorithm. Copyright © 2023, The Authors. All rights reserved.

关键词： Cost functions

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Mitigating Epilepsy by Stabilizing Linear Fractional-Order systems

Mitigating Epilepsy by Stabilizing Linear Fractional-Order S...

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American control Conference (ACC)

作者： Emily A. Reed Guilherme Ramos Paul Bogdan Sérgio Pequito Ming Hsieh Electrical and Computer Engineering Department University of Southern California USA Dept. of Computer Science and Engineering Instituto Superior Técnico University of Lisbon Portugal and Instituto de Telecomunicações Lisbon Portugal Department of Information Technology Division of Systems and Control Uppsala University Uppsala Sweden

Epilepsy affects approximately 50 million people worldwide. Despite its prevalence, the recurrence of seizures can be mitigated only 70% of the time through medication. Furthermore, surgery success rates range from 30% - 70% because of our limited understanding of how a seizure starts. However, one leading hypothesis suggests that a seizure starts because of a critical transition due to an instability. Unfortunately, we lack a meaningful way to quantify this notion that would allow physicians to not only better predict seizures but also to mitigate them. Hence, in this paper, we develop a method to not only characterize the instability of seizures but also to leverage these conditions to stabilize the system underlying these seizures. Remarkably, evidence suggests that such critical transitions are associated with long-term memory dynamics, which can be captured by considering linear fractional-order systems. Subsequently, we provide for the first time tractable necessary and sufficient conditions for the global asymptotic stability of discrete-time linear fractional-order systems. Next, we propose a method to obtain a stabilizing control strategy for these systems using linear matrix inequalities. Finally, we apply our methodology to a real-world epileptic patient dataset to provide insight into mitigating epilepsy and designing future cyber-neural systems.

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Approaches to building systems for collecting and processing data on the operation of technological equipment at industrial enterprises

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AIP Conference Proceedings 2024年第1期3021卷

作者： P. A. Nikishechkin A. N. Nikich Computer Control Systems Department Moscow State University of Technology «STANKIN» Moscow Russia

The article is devoted to the description of the main methods and approaches to building systems for collecting and processing data coming from technological equipment. Testing and comparative analysis of access methods to data coming from technological equipment was carried out, including the use of the «AxiOMA control» CNC API, the UA-Expert system based on the OPC protocol, access through network packet analysis using the example of the Sercos III protocol, a system based on Node-RED.

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S2KAN-SLAM: Elastic Neural LiDAR SLAM with SDF Submaps and Kolmogorov-Arnold Networks

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IEEE Transactions on Circuits and systems for Video technology 2025年

作者： Wang, Zhong Zhang, Lin Wang, Hesheng Shanghai Jiao Tong University State Key Laboratory of Avionics Integration and Aviation System-of-Systems Synthesis Department of Automation Key Laboratory of System Control and Information Processing of Ministry of Education Shanghai200240 China Tongji University School of Computer Science and Technology National Pilot Software Engineering School with Chinese Characteristics Shanghai201804 China

Traditional LiDAR SLAM approaches prioritize localization over mapping, yet high-precision dense maps are essential for numerous applications involving intelligent agents. Recent advancements have introduced methods leveraging neural fields to enhance mapping capabilities;however, these approaches still face several limitations. Firstly, concerning scene representation, they typically employ neural fields with high-dimensional features and multi-layer perceptron decoders utilizing non-continuous activation functions. This results in low learning efficiency and challenges in capturing high-frequency signals. Secondly, in terms of scene organization, these methods often treat the entire scene as a singular neural field, leading to inefficiencies, inflexibility, and difficulties in rectifying accumulated errors when mapping large-scale environments over extended periods. To tackle the first issue, we propose a lightweight continuous SDF regression approach by encoding the scene in single-valued embeddings and decoding SDF values from a Kolmogorov-Arnold Network. By minimizing discrepancies in measuring range, sampling distance, and decoded SDF values, we facilitate iterative frame-to-model tracking and bundle adjustment neural mapping. To mitigate the second challenge, we propose structuring the whole scene into multiple neural SDF submaps. By establishing node-node, node-submap, and loop closure constraints into a global pose graph, the system can create dense neural maps with global consistency across large-scale scenes. Experimental evaluations in both real-world and simulated settings indicate that our system achieves superior mapping completeness and accuracy, enhanced learning efficiency, reduced memory consumption, and greater flexibility compared to its counterparts. © 2025 IEEE.

关键词： Mapping

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Distributed Optimization with Gradient Descent and Quantized Communication

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IFAC-PapersOnLine 2023年第2期56卷 5900-5906页

作者： Apostolos I. Rikos Wei Jiang Themistoklis Charalambous Karl H. Johansson Division of Decision and Control Systems KTH Royal Institute of Technology SE-100 44 Stockholm Sweden Digital Futures SE-100 44 Stockholm Sweden Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia Cyprus

In this paper, we consider the unconstrained distributed optimization problem, in which the exchange of information in the network is captured by a directed graph topology, thus, nodes can only communicate with their neighbors. Additionally, in our problem, the communication channels among the nodes have limited bandwidth. In order to alleviate this limitation, quantized messages should be exchanged among the nodes. For solving this distributed optimization problem, we combine a gradient descent method with a distributed quantized consensus algorithm (which requires the nodes to exchange quantized messages and converges in a finite number of steps). Specifically, at every optimization step, each node (i) performs a gradient descent step (i.e., subtracts the scaled gradient from its current estimate), and (ii) performs a finite-time calculation of the quantized average of every node's estimate in the network. As a consequence, this algorithm approximately mimics the centralized gradient descent algorithm. We show that our algorithm asymptotically converges to a neighborhood of the optimal solution with linear convergence rate. The performance of the proposed algorithm is demonstrated via simple illustrative examples.

关键词： Distributed optimization quantized communication directed graphs finite-time consensus

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