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arXiv 2023年

作者： Caldas, Kenny A.Q. Barbosa, Filipe M. Silva, Junior A.R. Santos, Tiago C. Gomes, Iago P. Rosero, Luis A. Wolf, Denis F. Grassi, Valdir Department of Electrical and Computer Engineering São Carlos School of Engineering University of São Paulo São Carlos Brazil Division of Automatic Control Department of Electrical Engineering Linköping University Linköping Sweden Institute of Mathematics and Computer Science University of São Paulo São Carlos Brazil

Off-road driving operations can be a challenging environment for human conductors as they are subject to accidents, repetitive and tedious tasks, strong vibrations, which may affect their health in the long term. Therefore, they can benefit from a successful implementation of autonomous vehicle technology, improving safety, reducing labor costs and fuel consumption, and increasing operational efficiency. The main contribution of this paper is the experimental validation of a path tracking control strategy, composed of longitudinal and lateral controllers, on an off-road scenario with a fully-loaded heavy-duty truck. The longitudinal control strategy relies on a Non-Linear Model Predictive controller (NMPC), which considers the path geometry and simplified vehicle dynamics to compute a smooth and comfortable input velocity, without violating the imposed constraints. The lateral controller is based on a Robust Linear Quadratic Regulator (RLQR), which considers a vehicle model subject to parametric uncertainties to minimize its lateral displacement and heading error, as well as ensure stability. Experiments were carried out using a fully-loaded vehicle on unpaved roads in an open-pit mine. The truck followed the reference path within the imposed constraints, showing robustness and driving smoothness. © 2023, CC BY.

关键词： controllers

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A Vision Based Hardware-Software Real-Time control System for the Autonomous Landing of an UAV 1

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International Conference on computer Vision and Graphics, ICCVG 2020

作者： Blachut, Krzysztof Szolc, Hubert Wasala, Mateusz Kryjak, Tomasz Gorgon, Marek Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Krakow Poland

ISBN: (数字)9783030590062

ISBN: (纸本)9783030590055

In this paper we present a vision based hardware-software control system enabling the autonomous landing of a multirotor unmanned aerial vehicle (UAV). It allows for the detection of a marked landing pad in real-time for a 1280 720 @ 60 fps video stream. In addition, a LiDAR sensor is used to measure the altitude above ground. A heterogeneous Zynq SoC device is used as the computing platform. The solution was tested on a number of sequences and the landing pad was detected with 96% accuracy. This research shows that a reprogrammable heterogeneous computing system is a good solution for UAVs because it enables real-time data stream processing with relatively low energy consumption. © 2020, Springer Nature Switzerland AG.

关键词： System-on-chip

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Learning from demonstrations for autonomous soft-tissue retraction

arXiv

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arXiv 2021年

作者： Pore, Ameya Tagliabue, Eleonora Piccinelli, Marco Dall’Alba, Diego Casals, Alicia Fiorini, Paolo Department of Computer Science University of Verona Italy Automatic Control and Computer Engineering Department Universitat Politècnica de Catalunya Barcelona Spain

The current research focus in Robot-Assisted Minimally Invasive Surgery (RAMIS) is directed towards increasing the level of robot autonomy, to place surgeons in a supervisory position. Although Learning from Demonstrations (LfD) approaches are among the preferred ways for an autonomous surgical system to learn expert gestures, they require a high number of demonstrations and show poor generalization to the variable conditions of the surgical environment. In this work, we propose an LfD methodology based on Generative Adversarial Imitation Learning (GAIL) that is built on a Deep Reinforcement Learning (DRL) setting. GAIL combines generative adversarial networks to learn the distribution of expert trajectories with a DRL setting to ensure generalisation of trajectories providing human-like behaviour. We consider automation of tissue retraction, a common RAMIS task that involves soft tissues manipulation to expose a region of interest. In our proposed methodology, a small set of expert trajectories can be acquired through the da Vinci Research Kit (dVRK) and used to train the proposed LfD method inside a simulated environment. Results indicate that our methodology can accomplish the tissue retraction task with human-like behaviour while being more sample-efficient than the baseline DRL method. Towards the end, we show that the learnt policies can be successfully transferred to the real robotic platform and deployed for soft tissue retraction on a synthetic phantom. © 2021, CC BY.

关键词： Deep learning

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Machine Learning-Based Cascade Filtering System for fNIRS Data Analysis

Machine Learning-Based Cascade Filtering System for fNIRS Da...

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Progress in Applied Electrical Engineering (PAEE)

作者： Mariusz Pelc Dariusz Mikołajewski Tuukka Ruotsalo Luis A. Leiva Adam Sudoł Edward Jacek Gorzelańczyk Adam Łysiak Aleksandra Kawala-Sterniuk Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Opole Poland Institute of Computer Science Kazimierz Wielki University Bydgoszcz Poland University of Helsinki Finland University of Copenhagen Denmark Department of Computer Science University of Luxembourg Luxembourg Luxembourg Faculty of Natural and Technical Sciences University of Opole Opole Poland Institute of Philosophy Kazimierz Wielki University Bydgoszcz Poland

This paper presents a preliminary study on the use of machine learning-based methods to select the appropriate parameters of cascade filters in the analysis of brain signals recorded using functional infrared spectroscopy (fNIRS), which shows the level of oxygenation in the brain and, unlike EEG signals (showing electrical brain activity), are less prone to potential interference, disturbances or artifacts occurrence.

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Identification of Fast Time-varying Communication Channels Using the Preestimation Technique

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IFAC-PapersOnLine 2021年第7期54卷 351-356页

作者： Maciej Niedźwiecki Artur Gańcza Piotr Kaczmarek Faculty of Electronics Telecommunications and Computer Science Department of Automatic Control Gdańsk University of Technology ul. Narutowicza 11/12 Gdańsk Poland

Accurate identification of stochastic systems with fast-varying parameters is a challenging task which cannot be accomplished using model-free estimation methods, such as weighted least squares, which assume only that system coefficients can be regarded as locally constant. The current state-of-the-art solutions are based on the assumption that system parameters can be locally approximated by a linear combination of appropriately chosen basis functions. The paper shows that tracking performance of the resulting local basis function estimation algorithms can be further improved by means of regularization. The method is illustrated by an important recent application - identification of fast time-varying acoustic channels used in underwater communication.

关键词： identification of time-varying systems underwater acoustic communication channels local basis function approach regularization leave-one-out cross-validation

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A computer Vision-Based Solution for Assessing Prosthetic Leg Length Discrepancy

A Computer Vision-Based Solution for Assessing Prosthetic Le...

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E-Health and Bioengineering Conference (EHB)

作者： Otilia Zvorişteanu Dimitrie-Cristian Fodor Vasile-Ion Manta Neculai-Eugen Seghedin Ştefan-Daniel Achirei Dragoş-Florin Chitariu Dept. of Computer Science and Engineering Faculty of Automatic Control and Computer Engineering “Gheorghe Asachi” Technical University of Iaşi Iaşi Romania “Dr. Iacob Czihac” Military Emergency Clinical Hospital Iaşi Dept. of Digital Production Systems Faculty of Machine Manufacturing and Industrial Management “Gheorghe Asachi” Technical University of Iaşi Iaşi Romania

ISBN: (数字)9798331532147

ISBN: (纸本)9798331532154

This paper presents a novel computer vision-based approach for assessing leg length discrepancy (LLD) in individuals with prosthetic limbs. The proposed solution uses image processing techniques to detect markers placed on the patient's knee, prosthesis, and a reference wall, allowing for precise measurement of limb alignment. Through a comparative analysis of the initial reference position, set by a specialist, and the current limb positioning, the algorithm identifies discrepancies in leg length. The system employs a non-invasive methodology, utilizing an IP camera to capture images and communicate them via Wi-Fi to a computing unit for further analysis. Experimental validation, conducted on simulated LLDs ranging from 1mm to 10mm, demonstrates the system's high sensitivity and accuracy in detecting subtle changes in limb alignment. This approach offers a scalable, automated alternative to traditional manual methods, improving both the reliability and ease of prosthetic adjustments.

关键词： Legged locomotion Wireless communication Limbs Prosthetic limbs Current measurement Manuals Cameras Reliability IP networks Wireless fidelity

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Leveraging GPT-4 Capabilities for Developing Context-Aware, Personalized Chatbot Interfaces in E-commerce Customer Support Systems

Leveraging GPT-4 Capabilities for Developing Context-Aware, ...

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International Conference on Communications and Signal Processing

作者： Kathari Santosh Timur Kholmukhamedov M Sandeep Kumar Mohd Aarif Iskandar Muda B Kiran Bala Department of MBA CMR Institute of Technology Bengaluru Bengaluru India Automatic Control and Computer Engineering Department Turin Polytechnic University in Tashkent Tashkent Uzbekistan Publishing Global Research Network Noida India Universitas Sumatera Utara Medan Indonesia Department of Artificial Intelligence and Data Science K.Ramakrishnan college of Engineering Trichy

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In the realm of e-commerce customer support, the adoption of chatbots is on the rise, driven by a quest for heightened user interactions. This study introduces an inventive approach harnessing the advanced capabilities of GPT-4 to construct chatbot interfaces that are both context-aware and personalized. The main aim of this work is to revolutionize ecommerce customer support by developing intelligent and adaptable chatbot interfaces that are context-aware, personalized, and significantly enhance user experiences. Existing chatbot models struggle with context retention and personalization, limiting effectiveness in dynamic e-commerce environments despite the promise of automation. This innovative approach leverages GPT-4’s robust language processing, integrating it with user profiling systems for personalized responses grounded in user preferences and historical interactions. The method gives, diverse e-commerce dataset undergoes collection and pre-processing, followed by fine-tuning GPT-4 with an emphasis on context-awareness and personalized responses. The model is seamlessly integrated with e-commerce backend systems for real-time information, multimodal input support caters to varied user preferences. Results exhibit a significant 95% accuracy improvement, affirming the chatbot’s enhanced ability to comprehend user queries. By integrating personalization and advanced context retention, this study enhances user engagement, paving way for revolutionary intelligent and adaptable chatbot interfaces in ecommerce customer support.

关键词： Ethics Limiting Automation Biological system modeling Customer services Companies Chatbots

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Gradient Boosting and Deep Learning Models Approach to Forecasting Promotions Efficiency in FMCG Retail 19th

Gradient Boosting and Deep Learning Models Approach to Forec...

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19th International Conference on Artificial Intelligence and Soft Computing, ICAISC 2020

作者： Henzel, Joanna Sikora, Marek Department of Computer Networks and System Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology ul. Akademicka 16 Gliwice44-100 Poland

ISBN: (纸本)9783030615338

In the paper, different approaches to the problem of forecasting promotion efficiency are presented. For four defined indicators of promotion effect, prediction models using Gradient Boosting method and Deep Learning methods were trained. The comparison of the results is provided. The experiments were performed for three groups of products from a large grocery company. © 2020, Springer Nature Switzerland AG.

关键词： Forecasting

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Separate and conquer heuristic allows robust mining of contrast sets in classification, regression, and survival data

arXiv

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arXiv 2022年

作者： Gudyś, Adam Sikora, Marek Wróbel, Lukasz Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Institute of Innovative Technologies EMAG Leopolda 31 Katowice40-189 Poland

Identifying differences between groups is one of the most important knowledge discovery problems. The procedure, also known as contrast sets mining, is applied in a wide range of areas like medicine, industry, or economics. In the paper we present RuleKit-CS, an algorithm for contrast set mining based on separate and conquer - a well established heuristic for decision rule induction. Multiple passes accompanied with an attribute penalization scheme provide contrast sets describing same examples with different attributes, distinguishing presented approach from the standard separate and conquer. The algorithm was also generalized for regression and survival data allowing identification of contrast sets whose label attribute/survival prognosis is consistent with the label/prognosis for the predefined contrast groups. This feature, not provided by the existing approaches, further extends the usability of RuleKit-CS. Experiments on over 130 data sets from various areas and detailed analysis of selected cases confirmed RuleKit-CS to be a useful tool for discovering differences between defined groups. The algorithm was implemented as a part of the RuleKit suite available at GitHub under GNU AGPL 3 licence (https://***/adaa-polsl/RuleKit). © 2022, CC BY-NC-ND.

关键词： Diagnosis

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SCARI: Separate and conquer algorithm for action rules and recommendations induction

arXiv

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arXiv 2021年

作者： Sikoraa, Marek Matyszok, Pawel Wróbel, Lukasz Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland

This article describes an action rule induction algorithm based on a sequential covering approach. Two variants of the algorithm are presented. The algorithm allows the action rule induction from a source and a target decision class point of view. The application of rule quality measures enables the induction of action rules that meet various quality criteria. The article also presents a method for recommendation induction. The recommendations indicate the actions to be taken to move a given test example, representing the source class, to the target one. The recommendation method is based on a set of induced action rules. The experimental part of the article presents the results of the algorithm operation on sixteen data sets. As a result of the conducted research the Ac-Rules package was made available. Copyright © 2021, The Authors. All rights reserved.

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