41 research outputs found
Forecasting Parking Lots Availability: Analysis from a Real-World Deployment
Smart parking technologies are rapidly being deployed in cities and public/private places around the world for the sake of enabling users to know in real time the occupancy of parking lots and offer applications and services on top of that information. In this work, we detail a real-world deployment of a full-stack smart parking system based on industrial-grade components. We also propose innovative forecasting models (based on CNN-LSTM) to analyze and predict parking occupancy ahead of time. Experimental results show that our model can predict the number of available parking lots in a ±3% range with about 80% accuracy over the next 1-8 hours. Finally, we describe novel applications and services that can be developed given such forecasts and associated analysis
Spear or Shield: Leveraging Generative AI to Tackle Security Threats of Intelligent Network Services
Generative AI (GAI) models have been rapidly advancing, with a wide range of
applications including intelligent networks and mobile AI-generated content
(AIGC) services. Despite their numerous applications and potential, such models
create opportunities for novel security challenges. In this paper, we examine
the challenges and opportunities of GAI in the realm of the security of
intelligent network AIGC services such as suggesting security policies, acting
as both a ``spear'' for potential attacks and a ``shield'' as an integral part
of various defense mechanisms. First, we present a comprehensive overview of
the GAI landscape, highlighting its applications and the techniques
underpinning these advancements, especially large language and diffusion
models. Then, we investigate the dynamic interplay between GAI's spear and
shield roles, highlighting two primary categories of potential GAI-related
attacks and their respective defense strategies within wireless networks. A
case study illustrates the impact of GAI defense strategies on energy
consumption in an image request scenario under data poisoning attack. Our
results show that by employing an AI-optimized diffusion defense mechanism,
energy can be reduced by 8.7%, and retransmission count can be decreased from
32 images, without defense, to just 6 images, showcasing the effectiveness of
GAI in enhancing network security
On machine learning-based techniques for future sustainable and resilient energy systems
Permanently increasing penetration of converter-interfaced generation and renewable energy sources (RESs) makes modern electrical power systems more vulnerable to low probability and high impact events, such as extreme weather, which could lead to severe contingencies, even blackouts. These contingencies can be further propagated to neighboring energy systems over coupling components/technologies and consequently negatively influence the entire multi-energy system (MES) (such as gas, heating and electricity) operation and its resilience. In recent years, machine learning-based techniques (MLBTs) have been intensively applied to solve various power system problems, including system planning, or security and reliability assessment. This paper aims to review MES resilience quantification methods and the application of MLBTs to assess the resilience level of future sustainable energy systems. The open research questions are identified and discussed, whereas the future research directions are identified
Convolutional Neural Networks as 2-D systems
This paper introduces a novel representation of convolutional Neural Networks
(CNNs) in terms of 2-D dynamical systems. To this end, the usual description of
convolutional layers with convolution kernels, i.e., the impulse responses of
linear filters, is realized in state space as a linear time-invariant 2-D
system. The overall convolutional Neural Network composed of convolutional
layers and nonlinear activation functions is then viewed as a 2-D version of a
Lur'e system, i.e., a linear dynamical system interconnected with static
nonlinear components. One benefit of this 2-D Lur'e system perspective on CNNs
is that we can use robust control theory much more efficiently for Lipschitz
constant estimation than previously possible
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An EKF-Based Performance Enhancement Scheme for Stochastic Nonlinear Systems by Dynamic Set-Point Adjustment
YesIn this paper, a performance enhancement scheme has been investigated for a class of stochastic nonlinear systems via set-point adjustment. Considering the practical industrial processes, the multi-layer systematic structure has been adopted to achieve the control design requirements subjected to random noise. The basic loop control is given by PID design while the parameters have been fixed after the design phase. Alternatively, we can consider that there exists an unadjustable loop control. Then, the additional loop is designed for performance enhancement in terms of the tracking accuracy. In particular, a novel approach has been presented to dynamically adjust the set-points using the estimated states of the systems through extended Kalman filter (EKF). Minimising the entropy criterion, the parameters of the set-point adjustment controller can be optimised which will enhance the performance of the entire closed-loop systems. Based upon the presented scheme, the stochastic stability analysis has been given to demonstrate that the closed-loop tracking errors are bounded in probability one. To indicate the effectiveness of the presented control scheme, the numerical examples have been given and the simulation results imply that the designed systems are bounded and the tracking performance can be enhanced simultaneously. In summary, a new framework for system performance enhancement has been presented even if the loop control is unadjustable which forms the main contribution of this paper
Distributed Coverage Control of Constrained Constant-Speed Unicycle Multi-Agent Systems
This paper proposes a novel distributed coverage controller for a multi-agent system with constant-speed unicycle robots (CSUR). The work is motivated by the limitation of the conventional method that does not ensure the satisfaction of hard state- and input-dependent constraints and leads to feasibility issues for multi-CSUR systems. In this paper, we solve these problems by designing a novel coverage cost function and a saturated gradient-search-based control law. Invariant set theory and Lyapunov-based techniques are used to prove the state-dependent confinement and the convergence of the system state to the optimal coverage configuration, respectively. The controller is implemented in a distributed manner based on a novel communication standard among the agents. A series of simulation case studies are conducted to validate the effectiveness of the proposed coverage controller in different initial conditions and with control parameters. A comparison study in simulation reveals the advantage of the proposed method in terms of avoiding infeasibility. The experiment study verifies the applicability of the method to real robots with uncertainties. The development procedure of the method from theoretical analysis to experimental validation provides a novel framework for multi-agent system coordinate control with complex agent dynamics
Disturbance/uncertainty estimation and attenuation techniques in PMSM drives–a survey
This paper gives a comprehensive overview on
disturbance/uncertainty estimation and attenuation (DUEA) techniques in permanent magnet synchronous motor (PMSM) drives.
Various disturbances and uncertainties in PMSM and also other alternating current (AC) motor drives are first reviewed which shows they have different behaviors and appear in different control loops of the system. The existing DUEA and other relevant control methods in handling disturbances and uncertainties widely used in PMSM drives, and their latest developments are then discussed and summarized. It also provides in-depth analysis of the relationship between these advanced control methods in the context of PMSM systems. When dealing with uncertainties,it is shown that DUEA has a different but complementary mechanism to widely used robust control and adaptive control. The similarities and differences in disturbance attenuation of DUEA and other promising methods such as internal model
control and output regulation theory have been analyzed in detail. The wide applications of these methods in different AC
motor drives (in particular in PMSM drives) are categorized and summarized. Finally the paper ends with the discussion on future
directions in this area
Privacy-preserving Intelligent Resource Allocation for Federated Edge Learning in Quantum Internet
Federated edge learning (FEL) is a promising paradigm of distributed machine
learning that can preserve data privacy while training the global model
collaboratively. However, FEL is still facing model confidentiality issues due
to eavesdropping risks of exchanging cryptographic keys through traditional
encryption schemes. Therefore, in this paper, we propose a hierarchical
architecture for quantum-secured FEL systems with ideal security based on the
quantum key distribution (QKD) to facilitate public key and model encryption
against eavesdropping attacks. Specifically, we propose a stochastic resource
allocation model for efficient QKD to encrypt FEL keys and models. In FEL
systems, remote FEL workers are connected to cluster heads via quantum-secured
channels to train an aggregated global model collaboratively. However, due to
the unpredictable number of workers at each location, the demand for secret-key
rates to support secure model transmission to the server is unpredictable. The
proposed systems need to efficiently allocate limited QKD resources (i.e.,
wavelengths) such that the total cost is minimized in the presence of
stochastic demand by formulating the optimization problem for the proposed
architecture as a stochastic programming model. To this end, we propose a
federated reinforcement learning-based resource allocation scheme to solve the
proposed model without complete state information. The proposed scheme enables
QKD managers and controllers to train a global QKD resource allocation policy
while keeping their private experiences local. Numerical results demonstrate
that the proposed schemes can successfully achieve the cost-minimizing
objective under uncertain demand while improving the training efficiency by
about 50\% compared to state-of-the-art schemes
Deep Reinforcement Learning with Importance Weighted A3C for QoE enhancement in Video Delivery Services
Adaptive bitrate (ABR) algorithms are used to adapt the video bitrate based
on the network conditions to improve the overall video quality of experience
(QoE). Recently, reinforcement learning (RL) and asynchronous advantage
actor-critic (A3C) methods have been used to generate adaptive bit rate
algorithms and they have been shown to improve the overall QoE as compared to
fixed rule ABR algorithms. However, a common issue in the A3C methods is the
lag between behaviour policy and target policy. As a result, the behaviour and
the target policies are no longer synchronized which results in suboptimal
updates. In this work, we present ALISA: An Actor-Learner Architecture with
Importance Sampling for efficient learning in ABR algorithms. ALISA
incorporates importance sampling weights to give more weightage to relevant
experience to address the lag issues with the existing A3C methods. We present
the design and implementation of ALISA, and compare its performance to
state-of-the-art video rate adaptation algorithms including vanilla A3C
implemented in the Pensieve framework and other fixed-rule schedulers like BB,
BOLA, and RB. Our results show that ALISA improves average QoE by up to 25%-48%
higher average QoE than Pensieve, and even more when compared to fixed-rule
schedulers.Comment: Number of pages: 10, Number of figures: 9, Conference name: 24th IEEE
International Symposium on a World of Wireless, Mobile and Multimedia
Networks (WoWMoM
DEUX: Active Exploration for Learning Unsupervised Depth Perception
Depth perception models are typically trained on non-interactive datasets
with predefined camera trajectories. However, this often introduces systematic
biases into the learning process correlated to specific camera paths chosen
during data acquisition. In this paper, we investigate the role of how data is
collected for learning depth completion, from a robot navigation perspective,
by leveraging 3D interactive environments. First, we evaluate four depth
completion models trained on data collected using conventional navigation
techniques. Our key insight is that existing exploration paradigms do not
necessarily provide task-specific data points to achieve competent unsupervised
depth completion learning. We then find that data collected with respect to
photometric reconstruction has a direct positive influence on model
performance. As a result, we develop an active, task-informed, depth
uncertainty-based motion planning approach for learning depth completion, which
we call DEpth Uncertainty-guided eXploration (DEUX). Training with data
collected by our approach improves depth completion by an average greater than
18% across four depth completion models compared to existing exploration
methods on the MP3D test set. We show that our approach further improves
zero-shot generalization, while offering new insights into integrating robot
learning-based depth estimation