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48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Kuang, Haopeng Yang, Dingkang Wang, Shunli Wang, Xiaoying Zhang, Lihua Zhongshan Hospital Fudan University Liver Surgery Department Shanghai China Fudan University Academy for Engineering & Technology Shanghai China Ministry of Education Engineering Research Center of Ai & Robotics Shanghai China

ISBN: (纸本)9781728163277

Accurate visualization of liver tumors and their surrounding blood vessels is essential for noninvasive diagnosis and prognosis prediction of tumors. In medical image segmentation, there is still a lack of in-depth research on the simultaneous segmentation of liver tumors and peritumoral blood vessels. To this end, we collect the first liver tumor, and vessel segmentation benchmark datasets containing 52 portal vein phase computed tomography images with liver, liver tumor, and vessel annotations. In this case, we propose a 3D U-shaped Cross-Attention Network (UCA-Net) that utilizes a tailored cross-attention mechanism instead of the traditional skip connection to effectively model the encoder and decoder feature. Specifically, the UCA-Net uses a channel-wise cross-attention module to reduce the semantic gap between encoder and decoder and a slice-wise cross-attention module to enhance the contextual semantic learning ability among distinct slices. Experimental results show that the proposed UCA-Net can accurately segment 3D medical images and achieve state-of-the-art performance on the liver tumor and intrahepatic vessel segmentation task. © 2023 IEEE.

关键词： cross attention Liver tumor segmentation skip connection vascular segmentation

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The role of artificial intelligence and IoT in prediction of earthquakes:Review

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Artificial Intelligence in Geosciences 2024年第1期5卷 154-172页

作者： Joshua Pwavodi Abdullahi Umar Ibrahim Pwadubashiyi Coston Pwavodi Fadi Al-Turjman Ali Mohand-Said Ecole et Observatoire des Sciences de La Terre ITES UMR7063-CNRS/Universit’e de Strasbourg5 rue Rene Descartes67084StrasbourgFrance Research Center for Science Technology and Engineering(BILTEM)Near East University99138NicosiaTRNCMersin 10Turkey Department of Biomedical Engineering Near East UniversityMersin 10Nicosia99010Turkey Department of Biomedical Engineering Faculty of EngineeringCyprus International UniversityHaspolatNorth Cyprus via Mersin 10Nicosia99258Turkey Artificial Intelligence Engineering Department AI and Robotics InstituteNear East UniversityMersin99138Turkey Research Center for AI and IoT Faculty of EngineeringUniversity of KyreniaMersin 99138Turkey

Earthquakes are classified as one of the most devastating natural disasters that can have catastrophic effects on the environment,lives,and *** has been an increasing interest in the prediction of earthquakes and in gaining a comprehensive understanding of the mechanisms that underlie their generation,yet earthquakes are the least predictable natural *** data,global positioning system,interferometry synthetic aperture radar(InSAR),and seismometers such as microelectromechanical system,seismometers,ocean bottom seismometers,and distributed acoustic sensing systems have all been used to predict earthquakes with a high degree of *** advances in seismic wave recording,storage,and analysis,earthquake time,location,and magnitude prediction remain *** the other hand,new developments in artificial intelligence(ai)and the Internet of Things(IoT)have shown promising potential to deliver more insights and ***,this article reviewed the use of ai-driven Models and IoT-based technologies for the prediction of earthquakes,the limitations of current approaches,and open research *** review discusses earthquake prediction setbacks due to insufficient data,inconsistencies,diversity of earthquake precursor signals,and the earth’s geophysical ***,this study examines potential approaches or solutions that scientists can employ to address the challenges they face in earthquake *** analysis is based on the successful application of ai and IoT in other fields.

关键词： Earthquakes Seismicity Artificial intelligence Internet of things Prediction

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A Consulting System for Portfolio Assets Allocation by Selecting the Best Agent in the Short Term Based on Cumulative Returns with Deep Reinforcement Learning 11

A Consulting System for Portfolio Assets Allocation by Selec...

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11th International Symposium on Telecommunication, IST 2024

作者： Movassaghpour, Shahin Sharbaf Kargar, Masoud Bayani, Ali Assadzadeh, Alireza Khakzadi, Ali Islamic Azad University Tabriz Branch Department of Computer Engineering Tabriz Iran Islamic Azad University Robotics and Soft Technologies Research Center Tabriz Branch Tabriz Iran

ISBN: (纸本)9798350356250

This paper proposes a sophisticated consulting system designed to optimize investment portfolios using deep reinforcement learning (DRL). The system includes a unique decision-making mechanism that selects the best-performing agent in the short term based on cumulative returns. We employ five different DRL agents: Advantage Actor-Critic (A2C), Soft Actor-Critic (SAC), Twin Delayed Deep Deterministic Policy Gradient (TD3), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO), to allocate portfolio weights dynamically. By evaluating each agent's performance over the previous ten days, our system adaptively selects the agent most likely to maximize returns in the immediate future. This approach enhances portfolio performance by leveraging the strengths of multiple DRL agents. The effectiveness of our proposed method is validated using historical data from the Dow Jones index, demonstrating significant improvements in cumulative returns and risk-adjusted performance metrics. Our results show that the main proposed method achieved the highest annual return of 11.43%, cumulative returns of 38.29%, and a Sharpe ratio of 0.832, outperforming individual DRL agents and highlighting the potential of DRL in advancing portfolio management strategies. © 2024 IEEE.

关键词： Deep reinforcement learning

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Belief Scene Graphs: Expanding Partial Scenes with Objects through Computation of Expectation

Belief Scene Graphs: Expanding Partial Scenes with Objects t...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Mario A.V. Saucedo Akash Patel Akshit Saradagi Christoforos Kanellakis George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics & AI Team Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In this article, we propose the novel concept of Belief Scene Graphs, which are utility-driven extensions of partial 3D scene graphs, that enable efficient high-level task planning with partial information. We propose a graph-based learning methodology for the computation of belief (also referred to as expectation) on any given 3D scene graph, which is then used to strategically add new nodes (referred to as blind nodes) that are relevant to a robotic mission. We propose the method of Computation of Expectation based on Correlation Information (CECI), to reasonably approximate real Belief/Expectation, by learning histograms from available training data. A novel Graph Convolutional Neural Network (GCN) model is developed, to learn CECI from a repository of 3D scene graphs. As no database of 3D scene graphs exists for the training of the novel CECI model, we present a novel methodology for generating a 3D scene graph dataset based on semantically annotated real-life 3D spaces. The generated dataset is then utilized to train the proposed CECI model and for extensive validation of the proposed method. We establish the novel concept of Belief Scene Graphs (BSG), as a core component to integrate expectations into abstract representations. This new concept is an evolution of the classical 3D scene graph concept and aims to enable high-level reasoning for task planning and optimization of a variety of robotics missions. The efficacy of the overall framework has been evaluated in an object search scenario, and has also been tested in a real-life experiment to emulate human common sense of *** a video of the article, showcasing the experimental demonstration, please refer to the following link: https://***/hsGlSCa12iY

关键词： Training Solid modeling Histograms Three-dimensional displays Computational modeling Robot sensing systems Search problems

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Autonomous Residual Threshold Detection for Decentralized Radar Inertial Odometry in Hostile Environments

Autonomous Residual Threshold Detection for Decentralized Ra...

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International Conference on Control, Automation and Diagnosis (ICCAD)

作者： Moumita Mukherjee Avijit Banerjee Anton Koval George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350361025

ISBN: (纸本)9798350361032

The article introduces an adaptive threshold detection mechanism aimed at enhancing a decentralized radar inertial odometry (RIO) framework, enabling resilient localization in challenging hostile environments. Considering that frequency-modulated continuous-wave (FMCW) radars possess characteristics enabling measurements in challenging indoor and outdoor environments, the fusion of multiple radars in an ensemble configuration, along with an inertial measurement unit (IMU), holds promise in surpassing individual sensor limitations. This approach thereby enhances robust perception. The proposed adaptive autonomous residual threshold mechanism employs a real-time residual analysis to dynamically adjust the sensor fusion process by comparing the variance between two extended Kalman filters. This adaptive approach addresses irregularities in data samples from multiple sensors, thereby enhancing the decentralized smoothing estimator’s precision in providing localization while navigating through hostile environments marked by limited visibility, extreme weather, or high interference. Consequently, it contributes to the resilience and adaptability of autonomous systems in real-world scenarios. The proposed framework effectively showcases precise localization through decentralized radar inertial odometry(RIO).

关键词： Location awareness Smoothing methods Radar measurements Radar detection Radar Sensor fusion Robot sensing systems

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Reinforcement Learning Driven Multi-Robot Exploration via Explicit Communication and Density-Based Frontier Search

arXiv

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arXiv 2024年

作者： Calzolari, Gabriele Sumathy, Vidya Kanellakis, Christoforos Nikolakopoulos, George the Robotics and AI Group Department of Computer Science Electrical and Space Engineering Luleå University of Technology Sweden

Collaborative multi-agent exploration of unknown environments is crucial for search and rescue operations. Effective real-world deployment must address challenges such as limited inter-agent communication and static and dynamic obstacles. This paper introduces a novel decentralized collaborative framework based on Reinforcement Learning to enhance multi-agent exploration in unknown environments. Our approach enables agents to decide their next action using an agent-centered field-of-view occupancy grid, and features extracted from A∗ algorithm-based trajectories to frontiers in the reconstructed global map. Furthermore, we propose a constrained communication scheme that enables agents to share their environmental knowledge efficiently, minimizing exploration redundancy. The decentralized nature of our framework ensures that each agent operates autonomously, while contributing to a collective exploration mission. Extensive simulations in Gymnasium and real-world experiments demonstrate the robustness and effectiveness of our system, while all the results highlight the benefits of combining autonomous exploration with inter-agent map sharing, advancing the development of scalable and resilient robotic exploration systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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RecNet: An Invertible Point Cloud Encoding through Range Image Embeddings for Multi-Robot Map Sharing and Reconstruction

RecNet: An Invertible Point Cloud Encoding through Range Ima...

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IEEE International Conference on robotics and Automation (ICRA)

作者： Nikolaos Stathoulopoulos Mario A.V. Saucedo Anton Koval George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In the field of resource-constrained robots and the need for effective place recognition in multi-robotic systems, this article introduces RecNet, a novel approach that concurrently addresses both challenges. The core of RecNet’s methodology involves a transformative process: it projects 3D point clouds into range images, compresses them using an encoder-decoder framework, and subsequently reconstructs the range image, restoring the original point cloud. Additionally, RecNet utilizes the latent vector extracted from this process for efficient place recognition tasks. This approach not only achieves comparable place recognition results but also maintains a compact representation, suitable for sharing among robots to reconstruct their collective maps. The evaluation of RecNet encompasses an array of metrics, including place recognition performance, the structural similarity of the reconstructed point clouds, and the bandwidth transmission advantages, derived from sharing only the latent vectors. Our proposed approach is assessed using both a publicly available dataset and field experiments 1 confirming its efficacy and potential for real-world applications.

关键词： Point cloud compression Measurement Image coding Three-dimensional displays Bandwidth Vectors Multi-robot systems

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Classification of Adversarial Attacks Using Ensemble Clustering Approach

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computers, Materials & Continua 2023年第2期74卷 2479-2498页

作者： Pongsakorn Tatongjai Tossapon Boongoen Natthakan Iam-On Nitin Naik Longzhi Yang Center of Excellence in AI&Emerging Technologies School of ITMae Fah Luang UniversityChiang RaiThailand Department of Computer Science Aberystwyth UniversityAberystwythUnited Kingdom School of Informatics and Digital Engineering Aston UniversityBirminghamUnited Kingdom Department of Computer and Information Sciences Northumbria UniversityNewcastleUnited Kingdom

As more business transactions and information services have been implemented via communication networks,both personal and organization assets encounter a higher risk of *** safeguard these,a perimeter defence likeNIDS(network-based intrusion detection system)can be effective for known *** has been a great deal of attention within the joint community of security and data science to improve machine-learning based NIDS such that it becomes more accurate for adversarial attacks,where obfuscation techniques are applied to disguise patterns of intrusive *** current research focuses on non-payload connections at the TCP(transmission control protocol)stack level that is applicable to different network *** contrary to the wrapper method introduced with the benchmark dataset,three new filter models are proposed to transform the feature space without knowledge of class *** ECT(ensemble clustering based transformation)techniques,i.e.,ECT-Subspace,ECT-Noise and ECT-Combined,are developed using the concept of ensemble clustering and three different ensemble generation strategies,i.e.,random feature subspace,feature noise injection and their *** on the empirical study with published dataset and four classification algorithms,new models usually outperform that original wrapper and other filter alternatives found in the *** is similarly summarized from the first experiment with basic classification of legitimate and direct attacks,and the second that focuses on recognizing obfuscated *** addition,analysis of algorithmic parameters,i.e.,ensemble size and level of noise,is provided as a guideline for a practical use.

关键词： Intrusion detection adversarial attack machine learning feature transformation ensemble clustering

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BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detection and Localization

BOX3D: Lightweight Camera-LiDAR Fusion for 3D Object Detecti...

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Mediterranean Conference on Control and Automation (MED)

作者： Mario A.V. Saucedo Nikolaos Stathoulopoulos Vidya Sumathy Christoforos Kanellakis George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics & AI Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350395440

ISBN: (纸本)9798350395457

Object detection and global localization play a crucial role in robotics, spanning across a great spectrum of applications from autonomous cars to multi-layered 3D Scene Graphs for semantic scene understanding. This article proposes $BOX3D$ , a novel multi-modal and lightweight scheme for localizing objects of interest by fusing the information from RGB camera and 3D LiDAR. $BOX3D$ is structured around a three-layered architecture, building up from the local perception of the incoming sequential sensor data to the global perception refinement that covers for outliers and the general consistency of each object's observation. More specifically, the first layer handles the low-level fusion of camera and LiDAR data for initial 3D bounding box extraction. The second layer converts each LiDAR's scan 3D bounding boxes to the world coordinate frame and applies a spatial pairing and merging mechanism to maintain the uniqueness of objects observed from different viewpoints. Finally, BOX3D integrates the third layer that supervises the consistency of the results on the global map iteratively, using a point-to-voxel comparison for identifying all points in the global map that belong to the object. Benchmarking results of the proposed novel architecture are showcased in multiple experimental trials on public state-of-the-art large-scale dataset of urban environments.

关键词： Location awareness Point cloud compression Three-dimensional displays Laser radar Urban areas Merging Object detection

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Electro-Pneumatic Interface Framework for PAM-based Humanoid Robot Motion Control with EMG

Electro-Pneumatic Interface Framework for PAM-based Humanoid...

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Mediterranean Electrotechnical Conference (MELECON)

作者： Nathan Hiruy Vidya Sumathy Jakub Haluska George Nikolakopoulos Department of Computer Electrical and Space Engineering Robotics and AI Group Luleå University of Technology Luleå Sweden

ISBN: (数字)9798350387025

ISBN: (纸本)9798350387032

In this work, a real-time electro-pneumatic interface framework for emulating human muscular motion on a pneumatic-actuated muscle-based robot is presented. Pneumatic-actuated muscles are flexible actuators that contract and expand using pressure input and are widely used in medical and robotic research to generate motions that mimic human muscles. Electromyographic signals that measure the nerve stimulation of the muscles in an organism, can be used to emulate real-time muscular motion in Pneumatic Artificial Muscle based robots. Here, the proposed electro-pneumatic interface framework takes a real-time electromyographic signal as input and outputs corresponding pneumatic signals for actuating the pneumatic artificial muscles. The electromyographic sensors, attached to the human body, pick up the signals during motion, and the software electro-pneumatic interface computes the required differential pressure for the robot’s motion. In this approach, experimental data is used to model the transfer functions between variables such as the electromyographic signal, robot deflection angle, and differential pressure for the pneumatic artificial muscles. The proposed framework is experimentally validated on a humanoid robot made of pneumatic artificial muscles. The simulation and experiment results show the virtues of the proposed methodology.

关键词： Artificial muscles Humanoid robots Pneumatic systems Muscles Robot sensing systems Electromyography Real-time systems

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