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IEEE International Conference on Systems, Man and Cybernetics

作者： Simon Peter Khabusi Yo-Ping Huang Van Phong Vu Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan Department of Electrical Engineering National Penghu University of Science and Technology Penghu Taiwan Department of Automatic Control Ho Chi Minh City University of Technology and Education Ho Chi Minh City Vietnam

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Feature selection holds significant importance in knowledge mining as it plays a pivotal role in selecting and preserving the most informative features within a dataset while discarding irrelevant, redundant, or noisy attributes. This process contributes to enhancing model performance, reducing computational complexity, and refining interpretability, thus facilitating more accurate and efficient data analysis. In high-dimensional datasets, the necessity for feature selection becomes more pronounced due to the heightened risk of encountering the curse of dimensionality. Therefore, this study proposes a weighted fuzzy rough quickreduct (FRQR) feature selection approach employing feature weights to handle the equal situation problem inherent in FRQR. The proposed method is evaluated on ten publicly available datasets with feature sizes ranging from 2000 to 15154. The selected features are used to train and test random forest candidate models whose estimates are then combined according to the posterior probabilities by Bayesian Model Averaging (BMA). The performance of the model on the selected features is evaluated on four performance metrics. The essentiality of the selected features is further determined by comparing the model classification performance achieved on the non-selected features and all the dataset features. The results indicate competitiveness in the performance metric values achieved on selected features over the other two feature categories affirming the efficacy of the proposed method.

关键词： Data analysis Posterior probability Refining Rough sets Feature extraction Distance measurement Data models Noise measurement Data mining Diseases

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Traffic Pattern Classification in Smart Cities Using Deep Recurrent Neural Network

arXiv

引用

arXiv 2024年

作者： Ismaeel, Ayad Ghany Krishnadas, J. Manishankar, S. Natarajan, Yuvaraj Mahmood, Sarmad Nozad Alani, Sameer Shather, Akram H. Computer Technology Engineering College of Engineering Technology Al-Kitab University Kirkuk36001 Iraq Department of Computer Science and Engineering Sahrdaya College of Engineering and Technology Thrissur Kodakara680684 India Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology Coimbatore641062 India Electronic and Control Engineering Techniques Technical Engineering College Northern Technical University Kirkuk36001 Iraq Computer Center University of Anbar Ramadi55431 Iraq Department of Computer Engineering Technology Al-Kitab University Altun Kopru Kirkuk36001 Iraq

This paper examines the use of deep recurrent neural networks to classify traffic patterns in smart cities. We propose a novel approach to traffic pattern classification based on deep recurrent neural networks, which can effectively capture traffic patterns' dynamic and sequential features. The proposed model combines convolutional and recurrent layers to extract features from traffic pattern data and a SoftMax layer to classify traffic patterns. Experimental results show that the proposed model outperforms existing methods regarding accuracy, precision, recall, and F1 score. Furthermore, we provide an in-depth analysis of the results and discuss the implications of the proposed model for smart cities. The results show that the proposed model can accurately classify traffic patterns in smart cities with a precision of as high as 95%. The proposed model is evaluated on a real-world traffic pattern dataset and compared with existing classification methods. © 2024, CC BY-NC-ND.

关键词： Classification (of information)

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Energy-Aware Task Allocation for Teams of Multi-mode Robots

arXiv

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arXiv 2025年

作者： Ito, Takumi Funada, Riku Sampei, Mitsuji Notomista, Gennaro Department of Systems and Control Engineering Institute of Science Tokyo Tokyo152-8550 Japan Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada

This work proposes a novel multi-robot task allocation framework for robots that can switch between multiple modes, e.g., flying, driving, or walking. We first provide a method to encode the multi-mode property of robots as a graph, where the mode of each robot is represented by a node. Next, we formulate a constrained optimization problem to decide both the task to be allocated to each robot as well as the mode in which the latter should execute the task. The robot modes are optimized based on the state of the robot and the environment, as well as the energy required to execute the allocated task. Moreover, the proposed framework is able to encompass kinematic and dynamic models of robots alike. Furthermore, we provide sufficient conditions for the convergence of task execution and allocation for both robot models. Copyright © 2025, The Authors. All rights reserved.

关键词： Constrained optimization

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Exploiting Robots as Healthcare Resources for Epidemics Management and Support Caregivers 10

Exploiting Robots as Healthcare Resources for Epidemics Mana...

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10th Italian Workshop on Artificial Intelligence and Robotics, AIRO 2023

作者： Napoli, Christian Napoli, Christian Ponzi, Valerio Puglisi, Adriano Russo, Samuele Tibermacine, Imad Eddine Department of Computer Control and Management Engineering Sapienza University of Rome via Ariosto 25 Roma00185 Italy Institute for Systems Analysis and computer Science Italian National Research Council Via dei Taurini 19 Roma00185 Italy Department of Medical Surgical Sciences and Translational Medicine Sapienza University of Rome Via di Grottarossa 1035 Roma00189 Italy Department of Psychology Sapienza University of Rome via dei Marsi 78 Roma00185 Italy

The COVID-19 emergency has exposed the fragility of many Health Care Systems around the world. Two major critical factors have been related to the management of critical care accesses and the availability of healthcare operators. COVID-like diseases are generally transmitted by airborne pathogens that grant a high contagion rate and rapidity. Unfortunately, health operators and medical doctors are the designated first victims of such epidemics. Infected operators (symptomatic or not) must be put at rest due to the potential contagion risk for the patients. In the midst of global health catastrophes such as the COVID-19 pandemic, the healthcare sector is always looking for new ways to improve patient care while reducing the risk of disease transmission. This study takes an innovative approach to the integration of robotic technology in hospital settings in order to increase operational efficiency, protect healthcare personnel, and improve patient outcomes. This study digs into the strategic deployment of robotic resources, with an emphasis on their function in epidemic control and front-line caregiver assistance. We look at how these robots may do different duties, decreasing human-to-human contact and the possibility of viral transmission. We pave the road for a safer, more efficient, and resilient healthcare environment by using the potential of robots as important healthcare resources. © 2024 Copyright for this paper by its authors.

关键词： COVID-19

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LiDAR-based drone navigation with reinforcement learning

TechRxiv

引用

TechRxiv 2023年

作者： Miera, Pawel Szolc, Hubert Kryjak, Tomasz Embedded Vision Systems Group Computer Vision Laboratory Department of Automatic Control and Robotics AGH University of Science and Technology Al. Mickiewicza 30 Krakow30-059 Poland

Reinforcement learning is of increasing importance in the field of robot control and simulation plays a key role in this process. In the unmanned aerial vehicles (UAVs, drones), there is also an increase in the number of published scientific papers involving this approach. In this work, an autonomous drone control system was prepared to fly forward (according to its coordinates system) and pass the trees encountered in the forest based on the data from a rotating LiDAR sensor. The Proximal Policy Optimization (PPO) algorithm, an example of reinforcement learning (RL), was used to prepare it. A custom simulator in the Python language was developed for this purpose. The Gazebo environment, integrated with the Robot Operating System (ROS), was also used to test the resulting control algorithm. Finally, the prepared solution was implemented in the Nvidia Jetson Nano eGPU and verified in the real tests scenarios. During them, the drone successfully completed the set task and was able to repeatably avoid trees and fly through the forest. © 2023, CC BY.

关键词： Drones

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A Comparative Study of Compressive Sensing Algorithms for Hyperspectral Imaging Reconstruction

A Comparative Study of Compressive Sensing Algorithms for Hy...

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IEEE IVMSP Workshop

作者： Jon Alvarez Justo Daniela Lupu Milica Orlandić Ion Necoara Tor Arne Johansen Department of Electronic Systems Norwegian University of Science and Technology Department of Automatic Control and Systems Engineering University Politehnica Bucharest Department of Engineering Cybernetics Norwegian University of Science and Technology

ISBN: (数字)9781665478229

ISBN: (纸本)9781665478236

Hyperspectral Imaging comprises excessive data consequently leading to significant challenges for data processing, storage and transmission. Compressive Sensing has been used in the field of Hyperspectral Imaging as a technique to compress the large amount of data. This work addresses the recovery of hyperspectral images $ 2.5\times$ compressed. A comparative study in terms of the accuracy and the performance of the convex FISTA/ADMM in addition to the greedy gOMP/BIHT/CoSaMP recovery algorithms is presented. The results indicate that the algorithms recover successfully the compressed data, yet the gOMP algorithm achieves superior accuracy and faster recovery in comparison to the other algorithms at the expense of high dependence on unknown sparsity level of the data to recover.

关键词： Greedy algorithms Multidimensional signal processing Image coding Conferences Signal processing algorithms Data processing Sensors

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Efficient Hybrid Multi-Population Genetic Algorithm for Multi-UAV Task Assignment in Consumer Electronics Applications

引用

IEEE Transactions on Consumer Electronics 2025年

作者： Bai, Xiaoshan Jiang, Haoyu Li, Chao Ullah, Inam Dabel, Maryam M. Al Bashir, Ali Kashif Wu, Zongze Sam, Shuzhi Shenzhen University College of Mechatronics and Control Engineering National Engineering Laboratory for Big Data System Computing Technology Shenzhen518060 China Shenzhen University College of Mechatronics and Control Engineering Shenzhen518060 China Shenzhen University Shenzhen City Joint Laboratory of Autonomous Unmanned Systems and Intelligent Manipulation Shenzhen518060 China Tashkent State University of Economics Department of Artificial Intelligence Tashkent100066 Uzbekistan University of Hafr Al Batin Department of Computer Science and Engineering College of Computer Science and Engineering Saudi Arabia Manchester Metropolitan University Manchester United Kingdom Shenzhen518060 China National University of Singapore Department of Electrical and Computer Engineering Singapore117576 Singapore

In recent years, as people’s living standards have improved and consumption concepts have been transformed, the demand for purchasing consumer electronics online has continued to grow, further stimulating the development of the logistics industry. Consequently, how to deliver consumer electronics to households at minimal cost has become a crucial factor that limits the development of the consumer technology industry. To tackle this problem, this paper studies the task assignment problem for multiple initially dispersed UAVs to deliver products to target locations while minimizing their total operation time. Each UAV can continuously provide delivery services to multiple target locations within its limited loading capacity and operation time. To solve this problem, we propose several hybrid multipopulation genetic algorithms. First, a novel crossover operator for the genetic algorithms is designed, through which a single parent chromosome can generate offspring individually. Second, two mutation mechanisms are performed to increase gene diversity. Third, multiple local search strategies are employed to enhance the populations’ fitness during each iteration of evolution. An improved 2-opt local search strategy is applied to optimize individual chromosomes when their similarity with the current best chromosome falls below a prescribed threshold. Alternatively, local search strategies are utilized for 1-opt, 2h-opt and interchange processes. Combining local search strategies, genetic operators, and the multi-population mechanism leads to several hybrid multi-population genetic algorithms. Numerical simulations and experimental tests demonstrate that the hybrid multi-population genetic algorithm, integrated with the improved 2-opt and 1-opt local search strategies, exhibits superior performance among the designed hybrid genetic algorithms, the minimum marginal cost algorithm (MMA), and the existing popular Co-evolutionary Multi-population Genetic Algorithm (CMGA). In exp

关键词： Genetic algorithms

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Texture Analysis of Breast US Images Using Morphological Transforms, Hausdorff Dimension and Bagging Ensemble Method

Texture Analysis of Breast US Images Using Morphological Tra...

引用

Mediterranean Conference on control and Automation (MED)

作者： Gigi Tăbăcaru Simona Moldovanu Marian Barbu Department of Automatic Control and Electrical Engineering Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati Romania Computer Science and Information Technology Faculty of Automation Computers Electrical Engineering and Electronics “Dunarea de Jos” University of Galati The Modelling & Simulation Laboratory Dunarea de Jos University of Galati Galati Romania

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

In this work, an attempt is made for the first time to use the measurement pattern generated by morphological transformation quantified by Hausdorff fractal dimension (HFD) and classified with ensemble learning based on bagging. The proposed work uses three morphological transformations for image preprocessing: hit-and-miss transform (HMT), white (WHT), and black top-hat (BHT). The pattern texture of US breast images is described by extracting the HFD from the regions of interest (ROI) after the ultrasound (US) images have been preprocessed. The main objective of this study was achieved by comparatively analyzing the classification performance of features using the Random Forest (RF), Extra Trees (ET) classifier, and bagging ensemble method based on XGBoot classifier. In presented study, the XGBoost classifier and BHT image processing method give an accuracy of 89.8% in a binary classification, benign versus malignant breast cancer.

关键词： Ultrasonic imaging Transforms Feature extraction Time measurement Fractals Ensemble learning Image preprocessing

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Model predictive control for autonomous ground vehicles:a review

引用

Autonomous Intelligent Systems 2021年第1期1卷 32-48页

作者： Shuyou Yu Matthias Hirche Yanjun Huang Hong Chen Frank Allgöwer State Key Laboratory of Automotive Simulation and Control Jilin UniversityChangchun 130022China Department of Control Science and Engineering Jilin UniversityChangchunChina Institute for Systems Theory and Automatic Control University of StuttgartPfaffenwaldring 9Stuttgart 70569Germany Institute of Intelligent Vehicles Tongji UniversityShanghaiChina College of Electronic and Information Engineering Tongji UniversityShanghaiChina

This paper reviews model predictive control(MPC)and its wide applications to both single and multiple autonomous ground vehicles(AGVs).On one hand,MPC is a well-established optimal control method,which uses the predicted future information to optimize the control actions while explicitly considering *** the other hand,AGVs are able to make forecasts and adapt their decisions in uncertain ***,because of the nature of MPC and the requirements of AGVs,it is intuitive to apply MPC algorithms to *** are interesting not only for considering them alone,which requires centralized control approaches,but also as groups of AGVs that interact and communicate with each other and have their own controller *** calls for distributed control ***,a short introduction into the basic theoretical background of centralized and distributed MPC is ***,it comprehensively reviews MPC applications for both single and multiple ***,the paper highlights existing issues and future research directions,which will promote the development of MPC schemes with high performance in AGVs.

关键词： Model predictive control Autonomous intelligent system Autonomous ground vehicle

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GETAE: Graph Information Enhanced Deep Neural Network Ensemble ArchitecturE for Fake News Detection

arXiv

引用

arXiv 2024年

作者： Truica, Ciprian-Octavian Apostol, Elena-Simona Marogel, Marius Paschke, Adrian Computer Science and Engineering Department Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest Splaiul Independentei 313 Bucharest060042 Romania Academy of Romanian Scientists Ilfov 3 Bucharest050044 Romania Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 14 Berlin14195 Germany Fraunhofer Institute for Open Communication Systems Kaiserin-Augusta-Allee 31 Berlin10589 Germany

In today’s digital age, fake news has become a major problem that has serious consequences, ranging from social unrest to political upheaval. To address this issue, new methods for detecting and mitigating fake news are required. In this work, we propose to incorporate contextual and network-aware features into the detection process. This involves analyzing not only the content of a news article but also the context in which it was shared and the network of users who shared it, i.e., the information diffusion. Thus, we propose GETAE, Graph Information Enhanced Deep Neural Network Ensemble ArchitecturE for Fake News Detection, a novel ensemble architecture that uses textual content together with the social interactions to improve fake news detection. GETAE contains two Branches: the Text Branch and the Propagation Branch. The Text Branch uses Word and Transformer Embeddings and a Deep Neural Network based on feed-forward and bidirectional Recurrent Neural Networks ([Bi]RNN) for learning novel contextual features and creating a novel Text Content Embedding. The Propagation Branch considers the information propagation within the graph network and proposes a Deep Learning architecture that employs Node Embeddings to create novel Propagation Embedding. GETAE Ensemble combines the two novel embeddings, i.e., Text Content Embedding and Propagation Embedding, to create a novel Propagation-Enhanced Content Embedding which is afterward used for classification. The experimental results obtained on two real-world publicly available datasets, i.e., Twitter15 and Twitter16, prove that using this approach improves fake news detection and outperforms state-of-the-art models. © 2024, CC BY-NC-ND.

关键词： Graph embeddings

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