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arXiv 2024年

作者： Mura, Raffaele Floris, Giuseppe Scionis, Luca Piras, Giorgio Pintor, Maura Demontis, Ambra Giacinto, Giorgio Biggio, Battista Roli, Fabio University of Cagliari Dept. of Electrical and Electronic Engineering Cagliari Italy Sapienza University of Rome Department of Computer Engineering Rome Italy University of Genoa Department of Computer Science Bioengineering Robotics and Systems Engineering Genoa Italy

Gradient-based attacks are a primary tool to evaluate robustness of machine-learning models. However, many attacks tend to provide overly-optimistic evaluations as they use fixed loss functions, optimizers, step-size schedulers, and default hyperparameters. In this work, we tackle these limitations by proposing a parametric variation of the well-known fast minimum-norm attack algorithm, whose loss, optimizer, step-size scheduler, and hyperparameters can be dynamically adjusted. We re-evaluate 12 robust models, showing that our attack finds smaller adversarial perturbations without requiring any additional tuning. This also enables reporting adversarial robustness as a function of the perturbation budget, providing a more complete evaluation than that offered by fixed-budget attacks, while remaining efficient. © 2024, CC BY.

关键词： Machine learning

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Design and Development of a Small UGV for Object Retrieval in Domestic Environments

Design and Development of a Small UGV for Object Retrieval i...

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IEEE Southeastcon

作者： Bhaavin Kishore Jogeshwar Joshua Bohannon Hakki Erhan Sevil Department of Intelligent Systems and Robotics University of West Florida (UWF) Pensacola FL USA Department of Electrical and Computer Engineering University of West Florida (UWF) Pensacola FL USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The objective of this project is to develop an unmanned ground vehicle (UGV) prototype capable of retrieving small objects in domestic environments. As the robotics field continues to advance, the demand for autonomous systems that enhance daily life is rapidly increasing. Autonomous systems have the potential to improve efficiency by performing routine tasks, thereby reducing human effort and promoting convenience in household settings. This study explores the development of a small UGV designed to identify, navigate toward, and retrieve small objects while autonomously avoiding obstacles such as walls and furniture in domestic environments. The system utilizes neural networks for real-time object detection. The UGV design consists of an object retrieval mechanism suitable for handling items typically left scattered in domestic environments. The proof-of-concept results show the potential of designed UGV domestic, and could potentially enable further advancements in autonomous household assistance technologies.

关键词： Navigation Toy manufacturing industry Software algorithms Prototypes Object detection Grasping Land vehicles Real-time systems Planning Vehicle dynamics

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Importance of Search Engine Optimization in Digitalization - A Comprehensive Analysis

Importance of Search Engine Optimization in Digitalization -...

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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Tatikonda, Reshmi Ponnala, Jainath Tatikonda, Ramya Yendluri, Dileep Kumar Kempanna, M. Bhuvanesh, A. University of the Central Missouri Department of Computer Information Systems Warrensburg United States Jawaharlal Technological University Department of Electronics and Communication Engineering Hyderabad India University of the Cumberlands Department of Information Technology Williamsburg United States University of the Cumberlands Department of Computer Science Williamsburg United States Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Bangalore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350388800

This article examines the complex realm of search engine optimization and its importance in the era of digitalization. With an emphasis on different facets of search engine optimization, including optimizing on-page elements, off-page optimization, technology optimization, and optimizing content, this document thoroughly comprehends how enterprises can boost their online appearance and generate natural website visitors. Moreover, it explores the complexities of search engine optimization algorithms and advancements and reviews the available resources and methods for enhancement. Additionally, it covers recommended approaches and plans for the successful implementation of SEO. This article further explores upcoming patterns within search engine optimization, including enhancing voice search and incorporating AI and ML. Furthermore, it discusses potential hurdles and potential openings in SEO's future. Lastly, the study provides suggestions to explore further and improve optimizing search engines. It includes regional SEO, UX optimization, sound search, and socially conscious SEO practices. © 2024 IEEE.

关键词： Search engines

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Risk Management in Robotic Process Automation 2

Risk Management in Robotic Process Automation

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2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Yendluri, Dileep Kumar Thatikonda, Ramya Thatikonda, Reshmi Ponnala, Jainath Kempanna, M. Bhuvanesh, A. University of the Cumberlands Department of Computer Science Williamsburg United States University of the Cumberlands Department of Information Technology Williamsburg United States University of Central Missouri Department of Computer Information Systems Williamsburg United States Jawaharlal Technological University Department of Electronics and Communication Engineering Hyderbad India Bangalore Institute of Technology Department of Artificial Intelligence and Machine Learning Bangalore India Psn College of Engineering and Technology Department of Electrical and Electronics Engineering Tirunelveli India

ISBN: (纸本)9798350360523

The paper investigates the nuances of RPA risk management, detailing the difficulties, tendencies, and approaches organizations must adopt to integrate RPA into their operations successfully. From automation to optimization, RPA's impact on industry workflow is revolutionary. However, RPA introduces a spectrum of technical, operational, business, and compliance risks alongside its benefits. Amidst the intricate landscape of RPA, this paper sheds light on key risks. It offers practical strategies for tackling them, enabling organizations to confidently unlock automation's transformative power. Trends like advanced analytics fusion, AI-driven risk analysis, and blockchain utilization will likely influence future RPA risk management. In addition, the paper discusses evolving challenges, including ethical issues, breakneck technology evolution, and digital security dangers. Using an in-depth examination of RPA risk management, this paper prepares organizations for the ever-changing automation environment with informed decisions and proactive measures. © 2024 IEEE.

关键词： Risk assessment

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Detecting End Effector Command Pose Error Using Perfect Bit Field Imaging

Detecting End Effector Command Pose Error Using Perfect Bit ...

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International Carpathian Control Conference (ICCC)

作者： Tomáš Buchta Jaroslav Hlava Tomáš Martinec Jaroslav Buchta Yegor Boyarchikov Department of Mechatronic Systems and Robotics Technical University of Liberec Liberec Czechia Institute of Mechatronics and Computer Engineering Technical University of Liberec Liberec Czechia

ISBN: (数字)9798331501273

ISBN: (纸本)9798331501280

This paper presents a novel approach to improving the precision of robotic arm operations in complex environments by using Perfect Bit Field Imaging (PBFI) for end effector command pose error detection. Accurate pose determination is essential for tasks such as manipulation, assembly, and autonomous navigation, particularly in space or hazardous settings where precision is critical. PBFI leverages high-resolution industrial cameras to capture detailed pixel-level information that can be used to correct the end effector’s position. The system’s key advantage is its low-cost nature, making it an attractive alternative to expensive sensor-based approaches. Advanced image processing techniques are used to interpret bit fields, allowing for quick and reliable pose corrections that reduce the need for recalibration and minimize the impact of external disturbances. Additionally, the approach is adaptable to various robotic arm configurations and operational conditions, making it suitable for a wide range of industrial and space-based applications. This research opens new avenues for enhancing robotic precision in challenging environments, contributing to the advancement of cost-effective autonomous systems.

关键词： Accuracy Service robots Image processing Robot vision systems Refining End effectors Real-time systems Error correction Robots Optimization

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Power Flow Optimization Redesign for Transient Stability Enhancement

Power Flow Optimization Redesign for Transient Stability Enh...

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Innovative Smart Grid Technologies (ISGT)

作者： Rahul Chakraborty Aranya Chakrabortty Denis Osipov Joe H. Chow Department of Electrical and Computer Engineering North Carolina State University USA Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute USA

We develop an optimization program referred to as the $H_{2}$ -power flow modification $(H_{2}$ -pfm) that utilizes the potential of inverter-based resources (IBRs) for improving damping of the electro-mechanical dynamics of a power system, and, in turn, enhances its transient stability radius as well. The method tunes the active and reactive power setpoints of the IBRs generated from the standard AC-OPF solution to optimize the $\mathbf{H}_{2}$ -norm of the transfer function of the grid model considering any incoming disturbance as the input and the generator rotor speeds as the outputs. Power transfer distribution factors (PTDFs) are used to identify the most relevant sets of tunable setpoints to reduce the search space, thereby making the problem solvable online. Simulation results are shown using a 16-machine, 68-bus New York power system model integrated with 7 wind farms. Results indicate that $H_{2}$ - PFM leads to notable improvement in transient stability compared to that for nominal AC-OPF.

关键词： Stability criteria Power system dynamics Transfer functions Power system stability Wind farms Minimization Transient analysis

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Review of Inference Time Prediction Approaches of DNN: Emphasis on Service robots with cloud-edge-device architecture

Review of Inference Time Prediction Approaches of DNN: Empha...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Xiang, Tian Meng, Qiwei Zhang, Ji Zhang, Beibei Song, Wei Xie, Anhuan Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China The Chinese University of Hong Kong T Stone Robotics Institute Department of Mechanical and Automation Engineering Hong Kong Research Institute of Intelligent Computing Zhejiang Laboratory Research Center for Intelligent Computing Software Hangzhou311100 China Dalhousie University Electrical and Computer Engineering Halifax Canada

ISBN: (纸本)9798350325706

In recent years, the global robot market has witnessed substantial growth, particularly in the domain of service robots. Despite their expanding presence, service robots encounter limitations when operating autonomously in unstructured environments, primarily due to their constrained computational capacities. As a solution, the fusion of cloud and edge computing resources becomes imperative to expedite task inference and enhance scenario perception capabilities. The integration of cloud-edge-device models holds significant promise in bolstering the operational efficiency of robots. This entails the dynamic partitioning of intricate robotic tasks, executed collaboratively across cloud, edge, and device resources. In this landscape, deep neural network (DNN) models play a pivotal role in facilitating a wide array of robotic tasks. The inference time for each layer of a DNN model in actual deployment, emerges as a critical determinant in the model's partitioning strategy. It also serves as an important metric influencing the model's suitability for a specific hardware platform. This article presents an overview of recent advancements in predicting inference and training time of DNN models, summarizes the related methods, and finally discusses the challenges in this field and the research that can be studied in the future. © 2023 IEEE.

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A Graph-Based Self-Calibration Technique for Cable-Driven Robots with Sagging Cable

A Graph-Based Self-Calibration Technique for Cable-Driven Ro...

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

作者： M. R. Dindarloo A. S. Mirjalili S. A. Khalilpour R. Khorrambakht Stephan Weiss H. D. Taghirad Advanced Robotics and Automated Systems (ARAS) Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Tandon School of Engineering Control/Robotics Research Laboratory New York University Brooklyn NY Department of Smart Systems Technologies University of Klagenfurt Austria

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

The efficient operation of large-scale Cable-Driven Parallel Robots (CDPRs) relies on precise calibration of kinematic parameters and the simplicity of the calibration process. This paper presents a graph-based self-calibration framework that explicitly addresses cable sag effects and facilitates the calibration procedure for large-scale CDPRs by only relying on internal sensors. A unified factor graph is proposed, incorporating a catenary cable model to capture cable sagging. The factor graph iteratively refines kinematic parameters, including anchor point locations and initial cable length, by considering jointly onboard sensor data and the robot’s kineto-static model. The applicability and accuracy of the proposed technique are demonstrated through Finite Element (FE) simulations, on both large and small-scale CDPRs subjected to significant initialization perturbations.

关键词： Meters Accuracy Perturbation methods Kinematics Robot sensing systems End effectors Software Calibration Sensors Finite element analysis

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Automated Warehouse 5G Infrastructure Modeling Using Variational Autoencoders

Automated Warehouse 5G Infrastructure Modeling Using Variati...

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International Symposium on Networks, computers and Communications (ISNCC)

作者： Rahul Gulia Amlan Ganguly Andres Kwasinski Michael E. Kuhl Ehsan Rashedi Clark Hochgraf Department of Computer Engineering Rochester Institute of Technology Rochester NY Department of Industrial and Systems Engineering Rochester Institute of Technology Rochester NY Department of Electrical and Computer Engineering Technology Rochester Institute of Technology Rochester NY

ISBN: (数字)9798350364910

ISBN: (纸本)9798350364927

The next decade is poised for a transformative shift in wireless communication technologies, driven by the increasing demand for data-intensive applications. Innovations in signal processing, network architecture estimation and design, and spectrum utilization will be critical in realizing the potential of 5G and beyond. These advancements will enable the seamless integration of emerging technologies and empower the digital transformation of industries and society. In this paper, we introduce a Variational Autoencoder (VAE) based model for indoor radio propagation modeling within an automated Industry4.0 warehouse or factory floor in the 5G wireless bands. We detail the creation of training data tensors, the architecture of our proposed VAE model, and its training using tensors that capture the impact of various interacting objects within a 5G-enabled smart warehouse infrastructure. The model is trained on multiple warehouse configurations to predict the signal-to-interference-plus-noise ratio (SINR) for both the validation dataset and unknown warehouse configurations. We present reconstruction error heatmaps to demonstrate the accuracy of our model and analyze its performance in different complex warehouse environments.

关键词： Analytical models Adaptation models Accuracy 5G mobile communication Indoor radio communication Training data Interference Predictive models Data models Signal to noise ratio

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An Efficient Federated Transfer Learning Approach for Multi-UAV systems

An Efficient Federated Transfer Learning Approach for Multi-...

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National Conference on Communications (NCC)

作者： Vedang Bhupesh Shenvi Nadkarni Sandeep Joshi L. Rajya Lakshmi Department of Computer Science and Information Systems Department of Electrical and Electronics Engineering Birla Institute of Technology and Science Pilani Rajasthan India

ISBN: (数字)9798331536015

ISBN: (纸本)9798331536022

Recent advances in multi-unmanned aerial vehicle (UAV) based federated learning do not take into consideration the massive computational requirements of modern deep learning models on mobile UAV s. Additionally, there has been significant progress that shows that the information transmitted between the federated agent and the central hub can be attacked to undermine the privacy of the data. We propose a novel multi-UAV-based federated transfer learning system that drastically reduces the computational burden overall, shifts it from UAV s to the ground fusion center, and reduces the bandwidth requirements while enhancing its secure nature. The proposed system makes multi-UAV learning significantly fast, reliable, power efficient, and practically feasible. Furthermore, we provide simulation and experimental results to demonstrate the effectiveness of the proposed system.

关键词： Deep learning Image segmentation Data privacy Federated learning Power supplies Transfer learning Object detection Autonomous aerial vehicles Data models Reliability

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