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IEEE Annual Conference on Decision and Control

作者： Marc J. Piggott Victor Solo School of Electrical Engineering and Telecommunications The University of New South Wales Sydney Australia

ISBN: (纸本)9781479978878

In recent years a number of novel distributed parameter estimation algorithms have been developed stimulated by applications in cognitive radio, robotics, wireless networks and sensor networks. However, existing performance analyses have used white noise assumptions extensively. Here, we analyse for the first time a class of diffusion LMS strategies under autocorrelation assumptions. Further, we obtain a result in the white noise setting which provides a new understanding of existing steady state mean square error results. We treat the Adapt-then-Combine (ATC) algorithm.

关键词： Mean square error adaptive algorithms white noise algorithms distributed parameter Parameter estimation

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Glucose Rate-of-Change and Insulin-on-Board Jointly Weighted Zone Model Predictive Control

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2023年第5期31卷 2261-2274页

作者： Deshpande, Sunil Doyle III, Francis J. J. Dassau, Eyal Harvard Univ Harvard John A Paulson Sch Engn & Appl Sci Boston MA 02134 USA

We present the design and evaluation of closed-loop insulin delivery using zone model predictive control (MPC) featuring an adaptive weighting scheme to address prolonged hyperglycemia due to changes in insulin sensitivity, underdelivery from profile mismatch, and meal composition. In the MPC cost function, the penalty on predicted glucose deviation from the upper zone boundary is weighted by a joint function of predicted glucose rate-of-change (ROC) and insulin-on-board (IOB). The asymmetric weighting gradually increases when glucose ROC and IOB are jointly low, independent of glucose magnitude, to limit hyperglycemia, while aggressively reducing for negative glucose ROC to avoid hypoglycemia. The proposed controller was evaluated using two simulation scenarios: an induced resistance scenario and a nominal scenario to highlight the performance over a reference zone MPC with glucose ROC weighting only. The continuous adaption scheme resulted in consistent improvement for the entire glucose range without incurring an additional risk of hypoglycemia. For the induced resistance and no feedforward bolus scenario, the percent time in 70-180 mg/dL was higher (53.5% versus 48.9%, p < 0.001) with a larger improvement in the overnight percent time in the tighter glucose range 70-140 mg/dL (70.9% versus 52.9%, p < 0.001). The results from extensive simulations, as well as clinical validation in three different outpatient studies, demonstrate the utility and safety of the proposed zone MPC.

关键词： adaptive algorithms closed-loop insulin delivery diabetes medical control systems model predictive control (MPC)

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Stability of Distributed adaptive algorithms I: Consensus algorithms.

Stability of Distributed Adaptive Algorithms I: Consensus Al...

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IEEE Annual Conference on Decision and Control

作者： Victor Solo School of Electrical Engineering and Telecommunications University of New South Wales Sydney AUSTRALIA

ISBN: (纸本)9781479978878

In recent years a number of novel distributed parameter estimation algorithms have been developed stimulated by applications in cognitive radio, robotics, sensor networks. However the performance analysis of these algorithms has only been developed under white noise assumptions and therefore does not apply to most applications. Here we develop analysis of a consensus based distributed LMS algorithm under some coloured noise assumptions.

关键词： Least mean square algorithm adaptive algorithms algorithms Parameter estimation Distribute Colored noise distributed parameter

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adaptive algorithms for Automated Intruder Detection in Surveillance Networks 3

Adaptive Algorithms for Automated Intruder Detection in Surv...

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3rd International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Ahmed, Tarem Pathan, Al-Sakib Khan Ahmed, Supriyo BRAC Univ Dept Elect & Elect Engn Dhaka Bangladesh Int Islamic Univ Malaysia Dept Comp Sci Kuala Lumpur Malaysia

ISBN: (纸本)9781479930807

Many types of automated visual surveillance systems have been presented in the recent literature. Most of the schemes require custom equipment, or involve significant complexity and storage needs. After studying the area in detail, this work presents four novel algorithms to perform automated, real-time intruder detection in surveillance networks. Built using machine learning techniques, the proposed algorithms are adaptive and portable, do not require any expensive or sophisticated component, are lightweight, and efficient with runtimes of the order of hundredths of a second. Two of the proposed algorithms have been developed by us. With application to two complementary data sets and quantitative performance comparisons with two representative existing schemes, we show that it is possible to easily obtain high detection accuracy with low false positives.

关键词： learning (artificial intelligence) real-time systems security of data video surveillance adaptive algorithms automated real-time intruder detection automated visual surveillance systems data sets machine learning techniques surveillance networks Educational institutions Image coding Read only memory

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Photonic-Assisted adaptive Multipath Self-Interference Cancellation Using Deep Reinforcement Learning

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JOURNAL OF LIGHTWAVE TECHNOLOGY 2023年第23期41卷 7183-7191页

作者： Yu, Xiao Ye, Jia Yan, Lianshan Zhou, Tao Zhu, Yue Li, Peng Zou, Xihua Pan, Wei Southwest Jiaotong Univ Ctr Informat Photon & Commun Sch Informat Sci & Technol Chengdu 611756 Peoples R China Southwest China Res Inst Elect Equipment Key Lab Elect Informat Control Chengdu 610036 Peoples R China Univ Ottawa Sch Elect Engn & Comp Sci Microwave Photon Res Lab Ottawa ON K1N6N5 Canada

Multipath self-interference (MSI) between the transmitter and receiver is an inevitable problem in realistic in-band full duplex (IBFD) transmissions. Due to the dynamic and intricate MSI channels, it is crucial for the MSI cancellation system to quickly obtain the optimal configuration of multi-dimensional parameters. To overcome this problem, a photonics-assisted adaptive MSI cancellation scheme using deep reinforcement learning (DRL) is proposed and experimentally demonstrated. Multiple local reference signals are obtained using optical wavelength division multiplexing to achieve MSI cancellation. An adaptive algorithm based on DRL is applied for adaptive optimization of multi-dimensional optical parameters to achieve real-time MSI cancellation. To manipulate the amplitude and time delay of the multiple-path local reference signals, a multipath optical tunable delay line (MOTDL) module is introduced. A proof-of-concept experiment was carried out to verify the feasibility of the proposed scheme. By superimposing three-path local reference signals, a MSI cancellation depth of 24 dB with a bandwidth of 1 GHz was achieved. After undergoing random exploration, the adaptive algorithm learns the state relationship between the MSI and multiple-path local reference signal. This facilitates achieving optimal cancellation within just 5 steps from most random starting point. In the experiment, a 16-QAM OFDM signal with a bandwidth of 600 MHz was successfully recovered from the MSI signal with a 1 GHz bandwidth in an IBFD transmission. Furthermore, the adaptive capability of the proposed algorithm is also validated in response to varying MSI conditions.

关键词： Optical transmitters Optical attenuators Optical receivers Photonics Optical modulation Optical sensors Optical scattering adaptive algorithms deep reinforcement learning microwave photonics multipath self-interference cancellation

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adaptive Method for Holographic Processing of Broadband Hydroacoustic Signals

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PHYSICS OF WAVE PHENOMENA 2024年第5期32卷 382-391页

作者： Kuz'kin, V. M. Pereselkov, S. A. Rybyanets, P. V. Tkachenko, S. F. Russian Acad Sci Prokhorov Gen Phys Ins Moscow 119991 Russia Voronezh State Univ Voronezh 394006 Russia

A new adaptive method is proposed for holographic processing of broadband hydroacoustic signals using vector-scalar receivers. The method allows underwater sources to be detected and localized by their noise field when information on the transfer function of the propagation environment is unavailable. It is simpler in implementation and more disturbance immune compared to earlier proposed adaptive algorithms. The results are presented that have been obtained in the numerical experiment on the stability of the method in the presence of intensive internal waves causing interaction of sound field modes of the noise source.

关键词： holographic signal processing adaptive algorithms source localization intensive internal waves hydroacoustic signals

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Accurate trajectory tracking control with adaptive neural networks for omnidirectional mobile robots subject to unmodeled dynamics

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2023年第1期45卷 1-11页

作者： Lima, Gabriel da Silva Moreira, Victor Ramon Firmo Bessa, Wallace Moreira Univ Fed Rio Grande do Norte Campus Univ Lagoa Nova BR-59078970 Natal RN Brazil Univ Turku Dept Mech & Mat Engn Joukahaisenkatu 3 Turku 20520 Finland

Omnidirectional mobile robots have gained a lot of attention in recent years due to their maneuverability capabilities. However, ensuring accurate trajectory tracking with this class of robots is still challenging control system designers. In this work, a novel intelligent controller is introduced for accurate trajectory tracking of omnidirectional robots subject to unstructured uncertainties. An adaptive neural network is adopted within a Lyapunov-based nonlinear control scheme to deal with frictional forces and other unmodeled dynamics or external disturbances that may occur. Online learning, rather than supervised offline training, is employed to allow the robot to learn on its own how to compensate for uncertainties and disturbances by interacting with the environment. The adoption of a combined error signal as the single input in the neural network significantly reduces the computational complexity of the disturbance compensation scheme and enables the resulting intelligent controller to be implemented in the embedded hardware of mobile robots. The boundedness and convergence properties of the proposed control scheme are proved by means of a Lyapunov-like stability analysis. The effectiveness of the proposed intelligent controller is numerically evaluated and experimentally validated using an omnidirectional mobile robot. The comparative analyses of the obtained results show that the adoption of an intelligent compensation scheme based on adaptive neural networks allows reductions of more than 95% in the tracking error, thus guaranteeing an accurate tracking and confirming the great superiority of the proposed control strategy.

关键词： adaptive algorithms Intelligent control Mobile robots Radial basis function networks

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adaptive algorithm for controlling the power management system on offshore jack-up drilling rigs

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Energy Sources, Part A: Recovery, Utilization and Environmental Effects 2024年第1期46卷 16717-16737页

作者： Cemeljic, Hrvoje Havelka, Juraj Jeremic, Aleksandar Kuzle, Igor Department of Energy and Power Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Electrical & Computer Engineering McMaster School of Biomedical Engineering McMaster University HamiltonON Canada Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

The power systems of offshore jack-up drilling rigs consist of diesel generators running in parallel load-sharing mode, controlled by an automatic Power Management System (PMS). In this paper, the operational performance of the diesel generators (DG) and the PMS on a jack-up drilling rig is investigated, focusing on two critical offshore drilling operations: "pipe tripping" and "pulling/pumping out of the hole" (POOH). During these operations, large power swings occur in the system, subjecting the DGs to sudden load surges. These severe imbalances in the system cause the PMS to intermittently start and stop the engines throughout the operation, leading to various problematic conditions in the power plant. The paper provides a novel solution for intermittent starting and stopping of the DGs in the form of development and implementation of an adaptive PMS algorithm, a mathematical model of the system and a machine learning approach to event prediction of PMS operation in offshore drilling. Research demonstrates a noteworthy decrease in intermittent DG starting and stopping when the adaptive algorithm is implemented, with a 96% reduction during pipe tripping and an 87% reduction during POOH. This improvement comes at the expense of an 11% increase in engine running hours and a 1.5% rise in fuel consumption. Furthermore, by configuring the adaptive algorithm to economy mode, savings of up to 1700 liters of fuel per month are achievable. © 2024 Taylor & Francis Group, LLC.

关键词： adaptive algorithms

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DYNAMIC AND adaptive algorithms OF COSMO-SKYMED SECONDA GENERAZIONE PLANNER

DYNAMIC AND ADAPTIVE ALGORITHMS OF COSMO-SKYMED SECONDA GENE...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Tiziana Scopa Giovanni Valentini Stefano Serva Silvia Mari Agenzia Spaziale Italiana Politecnico snc Ministry of Defense SGD/DNA

ISBN: (纸本)9781479979301

COSMO-SkyMed di Seconda Generazione (COSMO-SkyMed Second Generation, CSG) is an "end-to-end" and "dual use" (Civilian and Defence) Italian Earth Observation Space System conceived at the twofold objective of ensuring operational continuity to the current constellation (COSMO-SkyMed - CSK), while improving functionality and performances. CSG primary mission objective is to promptly and effectively satisfy the heterogeneous user needs (institutional, scientific and commercial purposes) producing a mission plan that satisfies the higher priority requests and optimizes the overall plan with the remaining lower priority requests according to the users programming rights consumption, while fully employing the system resources and taking into account both technical and managerial constraints. Indeed the CSG Mission Planning and Scheduling tool implements innovative adaptive planning algorithms based on both priority criteria and saturation of system resources that envisage two scheduling strategies, the rank-based and the optimization-based respectively. The CSG algorithms are implemented in an iterative dynamic process of finding optimal solutions able to better answer the demanding requirements coming from the heterogeneous users.

关键词： Mission Planning and Scheduling tool COSMO-SkyMed Seconda Generazione Dual use Dynamic and adaptive algorithms Operational Research Operations Research system resources adaptive algorithms dynamic algorithms

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Comprehensive Review of Advanced Machine Learning Techniques for Detecting and Mitigating Zero-Day Exploits

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2025年第1期12卷 1-10页

作者： Mohamed, Nachaat Taherdoost, Hamed Madanchian, Mitra Rabdan Acad Abu Dhabi U Arab Emirates Rabdan Acad Res & Innovat Ctr Abu Dhabi U Arab Emirates Global Univ Syst GUS Inst London EC1N 2LX England Univ Canada West Vancouver BC V6B 1V9 Canada Hamta Business Corp R&D Dept Vancouver BC Canada Quark Minded Technol Inc Q Minded Vancouver BC Canada

This paper provides an in-depth examination of the latest machine learning (ML) methodologies applied to the detection and mitigation of zero-day exploits, which represent a critical vulnerability in cybersecurity. We discuss the evolution of machine learning techniques from basic statistical models to sophisticated deep learning frameworks and evaluate their effectiveness in identifying and addressing zero-day threats. The integration of ML with other cybersecurity mechanisms to develop adaptive, robust defense systems is also explored, alongside challenges such as data scarcity, false positives, and the constant arms race against cyber attackers. Special attention is given to innovative strategies that enhance real-time response and prediction capabilities. This review aims to synthesize current trends and anticipate future developments in machine learning technologies to better equip researchers, cybersecurity professionals, and policymakers in their ongoing battle against zero day exploits.

关键词： Machine Learning Zero-Day Exploits Cybersecurity Threat Detection adaptive algorithms Deep Learning in Security

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