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arXiv 2023年

作者： Sidiropoulos, Antonis Doulgeri, Zoe Automation & Robotics Lab Dept. of Electrical & Computer Engineering Aristotle University of Thessaloniki Greece

DMP have been extensively applied in various robotic tasks thanks to their generalization and robustness properties. However, the successful execution of a given task may necessitate the use of different motion patterns that take into account not only the initial and target position but also features relating to the overall structure and layout of the scene. To make DMP applicable to a wider range of tasks and further automate their use, we design a framework combining deep residual networks with DMP, that can encapsulate different motion patterns of a planar task, provided through human demonstrations on the RGB image plane. We can then automatically infer from new raw RGB visual input the appropriate DMP parameters, i.e. the weights that determine the motion pattern and the initial/target positions. We compare our method against another SoA method for inferring DMP from images and carry out experimental validations in two different planar tasks. © 2023, CC BY.

关键词： Time and motion study

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Selection of Beamforming in 5G MIMO scenarios using Machine Learning approach 19

Selection of Beamforming in 5G MIMO scenarios using Machine ...

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19th International Conference on Electrical engineering/Electronics, computer, Telecommunications and Information Technology, ECTI-CON 2022

作者： Silva, Douglas Henrique Augusto Ribeiro, David Ramirez, Miguel Arjona Lopes Rosa, Renata Chaudhary, Sushank Rodriguez, Demostenes Zegarra Federal University of Lavras Dept. of Computer Science MG Lavras Brazil Dept. of Automation Federal University of Lavras MG Lavras Brazil University of São Paulo Dept. of Electronic Systems Engineering São Paulo Brazil Chulalongkorn University Dept. of Electrical Engineering Bangkok Thailand

ISBN: (纸本)9781665485845

In new generation networks, 5G and 6G networks, intelligent mechanisms based on artificial intelligence algorithms are playing a relevant role in the performance improvement at different network levels. In 5G networks, different techniques are used, such as MIMO systems, and its network infrastructure is utilized by diverse services, for instance, vehicular communications. Thus, the description and the tracing of a communication scenario needs a large volume of data. Because of the difficulty to implement actual 5G networks, there is a lack of datasets containing complete 5G scenarios, the data is not enough or contain imbalanced classes to properly train machine learning (ML) algorithms. In this context, we propose a method to increase the amount of data to improve the machine learning performance of some classification models, specifically Random Forest, Multilayer Perceptron, and k-Nearest Neighbors. In the experimental results of the test phase, considering the inclusion of synthetic data, Random Forest, Multilayer Perceptron and k-Nearest Neighbors reached macro F1 scores of 0.9341, 0.9241 and 0.9456, respectively, which are superior to the results obtained when training with the original data only. © 2022 IEEE.

关键词： MIMO systems

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Enforcing Constraints for Dynamic Obstacle Avoidance by Compliant Robots

Enforcing Constraints for Dynamic Obstacle Avoidance by Comp...

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IEEE International Conference on Robotics and automation (ICRA)

作者： Leonidas Koutras Konstantinos Vlachos George S. Kanakis Fotios Dimeas Zoe Doulgeri George A. Rovithakis Dept. of Electrical & Computer Engineering Automation & Robotics Lab Aristotle University of Thessaloniki Greece

In this work a control scheme is proposed to enforce dynamic obstacle avoidance constraints to the full body of actively compliant robots. We argue that both compliance and accuracy are necessary to build safe collaborative robotic systems; obstacle avoidance is usually not enough, due to the reliance on perception systems which exhibit delays and errors. Our scheme is able to successfully avoid obstacles, while remaining compliant in the entirety of the executed task. Therefore, in case of unexpected collisions due to perception system errors, the robot remains safe for humans and its environment. Our approach is validated through experiments with simulated and real obstacles utilizing a 7-dof KUKA LBR iiwa robotic manipulator.

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From RGB images to Dynamic Movement Primitives for planar tasks

From RGB images to Dynamic Movement Primitives for planar ta...

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IEEE-RAS International Conference on Humanoid Robots

作者： Antonis Sidiropoulos Zoe Doulgeri Dept. of Electrical & Computer Engineering Automation & Robotics Lab Aristotle University of Thessaloniki Greece

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Towards Passivity Based Nonprehensile Bimanual Manipulation of Large Objects

Towards Passivity Based Nonprehensile Bimanual Manipulation ...

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IEEE-RAS International Conference on Humanoid Robots

作者： Leonidas Koutras Ilia Ntoliou Zoe Doulgeri Dept. of Electrical & Computer Engineering Automation & Robotics Lab Aristotle University of Thessaloniki Greece

In this work a passivity based control scheme to manipulate large objects supported on planar surfaces by bimanual robots is introduced. A control input is designed to accomplish the rotation and displacement of the object to a desired pose without assuming any knowledge of the object dynamics and friction dynamics between the object and the supporting surface. Passivity arguments are used to rigorously prove its stability. The proposed method is evaluated in simulations with friction forces simulated using the elastoplastic model. Experimental results performed on a bimanual robot with two 7-dof KUKA LBR iiwa 7 arms manipulating a variety of large objects verify the theoretical results.

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Analysis of the PV Energy Potential in the Bulgarian Danube River Region between Ruse and Silistra

Analysis of the PV Energy Potential in the Bulgarian Danube ...

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Communications, Information, Electronic and Energy Systems (CIEES), International Conference on

作者： Katerina Gabrovska-Evstatieva Ivaylo Stoyanov Tsvetelina Kaneva Nikolay Valov Vladimir Tsankov Boris Evstatiev Nicolay Mihailov Dept. of Computer Science University of Ruse Ruse Bulgaria Dept. of Electric Power Engineering University of Ruse Ruse Bulgaria Dept. of Computer Systems and Technologies University of Ruse Ruse Bulgaria Dept. of Automation and Electronics University of Ruse Ruse Bulgaria

ISBN: (数字)9798350352863

ISBN: (纸本)9798350352870

The goal of the study is to assess and compare the photovoltaic energy potential in the central-eastern part of the Bulgarian Danube region between the cities of Ruse and Silistra. To achieve this the solar radiation and the temperature of the environment should be obtained and analyzed. The goal of the study is to assess and compare the photovoltaic energy potential in the central-eastern part of the Bulgarian Danube region between the cities of Ruse and Silistra. To achieve this the solar radiation and the temperature of the environment should be obtained and analyzed. The goal of the study is to assess and compare the photovoltaic energy potential in the central-eastern part of the Bulgarian Danube region between the cities of Ruse and Silistra. To achieve this the solar radiation and the temperature of the environment should be obtained and analyzed.

关键词： Photovoltaic systems Electric potential Temperature distribution Renewable energy sources Urban areas Rivers Solar radiation Thermal pollution

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Surrogate-Assisted Multi-Class Collaborative Teaching and Learning Optimizer for High-Dimensional Industrial Optimization Problems

Surrogate-Assisted Multi-Class Collaborative Teaching and Le...

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2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Bi, Jing Wang, Ziqi Yuan, Haitao Yang, Jinhong Zhang, Jia College of Computer Beijing University of Technology Beijing100124 China Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Cssc Systems Engineering Research Institute Beijing China Lyle School of Engineering Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9781665410205

Swarm intelligence and evolutionary algorithms are widely applied in industrial scheduling, mobile edge computing, etc due to their strong robustness and fast optimization speed. However, some real-world industrial optimization problems involve numerous decision variables, known as high-dimensional problems. Current algorithms often require considerable computational resources to evaluate objective function values because of high-dimensional decision spaces. Moreover, they are also prone to be trapped in local optima. To solve the above problems, this work proposes an improved algorithm named Surrogate-assisted Multi-class Collaborative Teaching and learning optimizer (SMCT). A multi-class collaborative teaching and learning optimizer is proposed as a base optimizer to improve exploration and exploitation abilities. Furthermore, an autoencoder-assisted radial basis function is proposed as the surrogate model to replace true function evaluations, thereby saving computational resources and balancing the complexity and accuracy in fitting true models. Finally, experimental results demonstrate that SMCT surpasses its existing peers in both search accuracy and convergence speed across eight high-dimensional benchmark functions. © 2024 IEEE.

关键词： Scheduling algorithms

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On the Enhancement of Energy Efficiency of Wireless Rechargeable Sensor Networks through Mobile Charger Scheduling

On the Enhancement of Energy Efficiency of Wireless Recharge...

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Distributed Computing and Optimization Techniques (ICDCOT), International Conference on

作者： Md. Kamaruzzaman Abhijit Chandra Md. Azharuddin Dept.of Robotics and Automation The Neotia University Kolkata India Dept. of Instrumentation and Electronics Engineering Jadavpur University Kolkata India Dept. of Computer Science and Engineering Aliah University Kolkata India

In recent years, wireless rechargeable sensor networks (WRSNs) have become more popular because of its diverse array of applications. However, the limited energy resources of individual sensors pose a significant challenge in maintaining network operations over an extended period. To address this challenge, this research paper presents an energy-efficient scheduling scheme based on distance based scheduling for mobile charging vehicle (MCV) in WRSNs. The proposed scheme capitalizes on the MCV, allowing them to dynamically adjust their charging schedules to cater to the energy demands of sensor nodes (SNs). By efficiently allocating charging tasks to MCV, the scheme aims to balance the energy distribution and mitigate the energy imbalance across the network. By conducting comprehensive simulations with various scenarios, the proposed scheme has been compared with some existing schemes in relation to charging path length, total energy received and charging utility. The simulation results manifest significant advantages of the proposed scheme.

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Classification of brain ischemia and hemorrhagic stroke using a hybrid method

Classification of brain ischemia and hemorrhagic stroke usin...

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Data Analytics for Business and Industry: Way Towards a Sustainable Economy (ICDABI), International Conference on

作者： Necip Çınar Buket Kaya Mehmet Kaya Dept. of Computer Engineering Dicle University Diyabakır Turkey Dept. of Electronics and Automation Fırat University Elazığ Turkey Dept. of Computer Engineering Fırat University Elazığ Turkey

ISBN: (数字)9798350369786

ISBN: (纸本)9798350369793

The availability of artificial intelligence applications has expanded with the development of extremely powerful computers. This speeds up the diagnosis of many ailments and allows for the application of effective treatments. This facilitates the work of numerous experts in the field of health, and more trustworthy outcomes are attained. Many researchers have started working in this topic as a result of how swiftly artificial intelligence has entered human life and the promising outcomes that have been obtained. The detection of brain strokes from CT images, which are the most prevalent in the world and a major threat to human life, is a field that is currently seeing a flurry of deep learning and machine learning-based approaches. This study attempted to distinguish between ischemic and hemorrhagic brain stroke types using CT images. Utilizing a hybrid methodology, multi-classification was carried out utilizing EfficientNetB0 and SVM model from ischemic, hemorrhagic, and normal brain CT images in our dataset. The purpose of this study is to demonstrate a higher classification performance by combining the feature extraction skills of deep learning networks with the classification capability of machine learning models. The ischemic, hemorrhagic, and normal CT images in our dataset were used to train the EfficientNet-B0 + SVM, EfficientNet-B0 + KNN, ResNet50 + SVM, ResNet50 + KNN, ResNet50, VGG19 + SVM, VGG19 + KNN, and VGG19 models. There was a triple classification. According to the study, the hybrid EfficientNet-B0 + SVM architecture had the highest load success rate. The model had 95.13%, 94.93, 95.06, and 94.94% accuracy, recall, precision, and F1 values, respectively. These findings demonstrated that the EfficientNet-B0 + SVM architecture is a highly effective method for classifying different types of brain strokes from CT images.

关键词： Support vector machines Deep learning Accuracy Computed tomography Nearest neighbor methods Feature extraction Brain modeling

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Cost-Effective and Dynamic Migration for Microservices in Hybrid Cloud-Edge Systems

Cost-Effective and Dynamic Migration for Microservices in Hy...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Zhai, Jiahui Bi, Jing Yuan, Haitao Zhang, Jia Beijing University of Technology Faculty of Information Technology Beijing100124 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Southern Methodist University Dept. of Computer Science Lyle School of Engineering DallasTX United States

ISBN: (纸本)9798350337020

Mobile edge computing (MEC), as a promising paradigm, delivers computation and storage capacities at the edge of the network. It supports delay-sensitive services for mobile users (MUs). However, dynamic and stochastic characteristics of MEC networks necessitate constant migration of installed services across edge servers to keep up with the mobility of MUs. As a result, the cost of maintaining the network increases significantly. Existing studies of MEC rarely consider the cost of service migration due to MU mobility. To minimize the long-term cost for microservices in a hybrid cloudedge system comprising of MUs, small base stations (SBSs), and a cloud data center (CDC), the total cost minimization is formulated as a constrained mixed-integer nonlinear program. To solve it, this work designs a novel meta-heuristic optimization algorithm called Multi-swarm Grey-wolf-optimizer based on Genetic-learning (MGG), which effectively combines strong local search capabilities of grey wolf optimizer with superior global search capabilities of genetic algorithm. MGG simultaneously optimizes service request routing among MUs, SBSs, and CDC, CPU speeds of SBSs, service deployment of SBSs, service migration cost of SBSs, as well as MUs' transmission power and channel bandwidth allocation. Simulation results with Google cluster trace demonstrate that MGG outperforms several state-of-the-art peers with respect to the overall cost of the hybrid system. © 2023 IEEE.

关键词： genetic algorithm grey wolf optimizer Mobile edge computing resource allocation service migration

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