The explosive development of mobile communications and networking has led to the creation of an extremely complex system,which is difficult to manage.Hence,we propose an AI-powered network framework that uses AI techn...The explosive development of mobile communications and networking has led to the creation of an extremely complex system,which is difficult to manage.Hence,we propose an AI-powered network framework that uses AI technologies to operate the network automatically.However,due to the separation between different mobile network operators,data barriers between diverse operators become bottlenecks to exploit the full power of AI.In this paper,we establish a mutual trust data sharing framework to break these data barriers.The framework is based on the distributed and temper-proof attributes of blockchain.We implement a prototype based on Hyperledger Fabric.The proposed system combines supervision and fine-grained data access control based on smart contracts,which provides a secure and trustless environment for data sharing.We further compare our system with existing data sharing schemes,and we find that our system provides a better functionality.展开更多
The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by phy...The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by physical attacks,EMP(electromagnetic pulse)events,or cyberattacks,such disruptions could cripple essential services like water supply,healthcare,communication,and transportation.Research indicates that an attack on just nine key substations could result in a coast-to-coast blackout lasting up to 18 months,leading to economic collapse,civil unrest,and a breakdown of public order.This paper explores the key vulnerabilities of the grid,the potential impacts of prolonged blackouts,and the role of AI(artificial intelligence)and ML(machine learning)in mitigating these threats.AI-driven cybersecurity measures,predictive maintenance,automated threat response,and EMP resilience strategies are discussed as essential solutions to bolster grid security.Policy recommendations emphasize the need for hardened infrastructure,enhanced cybersecurity,redundant power systems,and AI-based grid management to ensure national resilience.Without proactive measures,the nation remains exposed to a catastrophic power grid failure that could have dire consequences for society and the economy.展开更多
Urbanisation presents complex challenges,including optimising land use,managing transportation networks,and ensuring equitable resource distribution.Artificial intelligence(AI)offers transformative solutions that enab...Urbanisation presents complex challenges,including optimising land use,managing transportation networks,and ensuring equitable resource distribution.Artificial intelligence(AI)offers transformative solutions that enable data-driven urban planning to improve efficiency and address system interdependencies.Despite advancements across various domains,fragmented AI implementation has limited the ability to address broader urban challenges.This paper proposes an AI-powered framework for optimising and reshaping urban spaces,underpinned by interdisciplinary integration and human-centered design.The framework combines data-driven decision-making,adaptive technologies,and participatory mechanisms to address current gaps and foster more cohesive urban systems.By prioritising inclusivity,adaptability,and sustainability,this framework offers a path toward creating resilient and inclusive urban environments.Future research should explore multi-dimensional data integration,adaptive systems,and stakeholder engagement in realizing AI’s full potential in shaping the cities of tomorrow.展开更多
Intelligent robotic manufacturing systems are revolutionizing the production industry.These next-generation systems employ robots as actuators,multi-source sensors for perception,and artificial intelligence for decisi...Intelligent robotic manufacturing systems are revolutionizing the production industry.These next-generation systems employ robots as actuators,multi-source sensors for perception,and artificial intelligence for decision-making,aiming to execute routine manufacturing tasks with greater autonomy and flexibility.In footwear manufacturing,sole deburring presents a specific challenge in detecting defects and elaborating deburring paths,which skilled workers traditionally handle.The present research goes beyond solving such problems traditionally with computer vision and hard robot programming.Instead,it focuses on developing a learning structure mimicking human motion planning capability from vision inputs.Like humans who mentally visualize and predict a path before refining it in real-time,we want to give the robot the ability to predetermine the trajectory needed for a finishing task,exploiting only vision data.The system is designed to learn how to identify defects and directly correlate this information with motions by utilizing a latent space representation,transitioning from simple programmed responses to more adaptive and intelligent behaviors.We call it a self-supervised vision-proprioception model,an AI framework that autonomously learns to correlate visual observations to proprioceptive data(end effector trajectories)for effective task execution.This is achieved by integrating a vision-based latent space learning phase(learn to see),followed by a reinforcement learning stage,where the agent learns to associate the latent space with deburring actions in a simulated environment(learn to act).Recognizing the common performance degradation when transferring learned policies to real robots,this research also employs Sim-to-Real methods to bridge the reality gap(learn to transfer).Experimental results validate the whole approach.展开更多
文摘The explosive development of mobile communications and networking has led to the creation of an extremely complex system,which is difficult to manage.Hence,we propose an AI-powered network framework that uses AI technologies to operate the network automatically.However,due to the separation between different mobile network operators,data barriers between diverse operators become bottlenecks to exploit the full power of AI.In this paper,we establish a mutual trust data sharing framework to break these data barriers.The framework is based on the distributed and temper-proof attributes of blockchain.We implement a prototype based on Hyperledger Fabric.The proposed system combines supervision and fine-grained data access control based on smart contracts,which provides a secure and trustless environment for data sharing.We further compare our system with existing data sharing schemes,and we find that our system provides a better functionality.
文摘The national grid and other life-sustaining critical infrastructures face an unprecedented threat from prolonged blackouts,which could last over a year and pose a severe risk to national security.Whether caused by physical attacks,EMP(electromagnetic pulse)events,or cyberattacks,such disruptions could cripple essential services like water supply,healthcare,communication,and transportation.Research indicates that an attack on just nine key substations could result in a coast-to-coast blackout lasting up to 18 months,leading to economic collapse,civil unrest,and a breakdown of public order.This paper explores the key vulnerabilities of the grid,the potential impacts of prolonged blackouts,and the role of AI(artificial intelligence)and ML(machine learning)in mitigating these threats.AI-driven cybersecurity measures,predictive maintenance,automated threat response,and EMP resilience strategies are discussed as essential solutions to bolster grid security.Policy recommendations emphasize the need for hardened infrastructure,enhanced cybersecurity,redundant power systems,and AI-based grid management to ensure national resilience.Without proactive measures,the nation remains exposed to a catastrophic power grid failure that could have dire consequences for society and the economy.
文摘Urbanisation presents complex challenges,including optimising land use,managing transportation networks,and ensuring equitable resource distribution.Artificial intelligence(AI)offers transformative solutions that enable data-driven urban planning to improve efficiency and address system interdependencies.Despite advancements across various domains,fragmented AI implementation has limited the ability to address broader urban challenges.This paper proposes an AI-powered framework for optimising and reshaping urban spaces,underpinned by interdisciplinary integration and human-centered design.The framework combines data-driven decision-making,adaptive technologies,and participatory mechanisms to address current gaps and foster more cohesive urban systems.By prioritising inclusivity,adaptability,and sustainability,this framework offers a path toward creating resilient and inclusive urban environments.Future research should explore multi-dimensional data integration,adaptive systems,and stakeholder engagement in realizing AI’s full potential in shaping the cities of tomorrow.
基金partly carried out within the MICS(Made in Italy—Circular and Sustainable)Extended Partnership and received funding from Next-Generation EU(Nos.Italian PNRR—M4 C2,Invest 1.3—D.D.1551.11-10-2022,PE00000004),CUP MICS D43C22003120001.
文摘Intelligent robotic manufacturing systems are revolutionizing the production industry.These next-generation systems employ robots as actuators,multi-source sensors for perception,and artificial intelligence for decision-making,aiming to execute routine manufacturing tasks with greater autonomy and flexibility.In footwear manufacturing,sole deburring presents a specific challenge in detecting defects and elaborating deburring paths,which skilled workers traditionally handle.The present research goes beyond solving such problems traditionally with computer vision and hard robot programming.Instead,it focuses on developing a learning structure mimicking human motion planning capability from vision inputs.Like humans who mentally visualize and predict a path before refining it in real-time,we want to give the robot the ability to predetermine the trajectory needed for a finishing task,exploiting only vision data.The system is designed to learn how to identify defects and directly correlate this information with motions by utilizing a latent space representation,transitioning from simple programmed responses to more adaptive and intelligent behaviors.We call it a self-supervised vision-proprioception model,an AI framework that autonomously learns to correlate visual observations to proprioceptive data(end effector trajectories)for effective task execution.This is achieved by integrating a vision-based latent space learning phase(learn to see),followed by a reinforcement learning stage,where the agent learns to associate the latent space with deburring actions in a simulated environment(learn to act).Recognizing the common performance degradation when transferring learned policies to real robots,this research also employs Sim-to-Real methods to bridge the reality gap(learn to transfer).Experimental results validate the whole approach.
