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arXiv 2023年

作者： Heena, Ayesha Biradar, Nagashettappa Maroof, Najmuddin M. Bhatia, Surbhi Agarwal, Rashmi Prasad, Kanta Department of Electronics and Communication BKIT Bhalki Karnataka VTU Karnataka Belagavi India Department of Electronics and Communication KBN College of Engineering Kalaburagi Karnataka VTU Karnataka Belagavi India Department of Information Systems College of Computer Science and Information Technology King Faisal University Al Hasa Saudi Arabia Department of Computer Application Manav Rachna International Institute of Research and Studies Faridabad India Department of Computer Science GL Bajaj Group of Institutions Mathura Mathura India

The popularity of Artificial intelligence and machine learning have prompted researchers to use it in the recent researches. The proposed method uses K-Nearest Neighbor (KNN) algorithm for segmentation of medical images, extracting of image features for analysis by classifying the data based on the neural networks. Classification of the images in medical imaging is very important, KNN is one suitable algorithm which is simple, conceptual and computational, which provides very good accuracy in results. KNN algorithm is a unique user-friendly approach with wide range of applications in machine learning algorithms which are majorly used for the various image processing applications including classification, segmentation and regression issues of the image processing. The proposed system uses gray level co-occurrence matrix features. The trained neural network has been tested successfully on a group of echocardiographic images, errors were compared using regression plot. The results of the algorithm are tested using various quantitative as well as qualitative metrics and proven to exhibit better performance in terms of both quantitative and qualitative metrics in terms of current state -of- the-art methods in the related area. To compare the performance of trained neural network the regression analysis performed showed a good *** Codes computers © 2023, CC0.

关键词： Regression analysis

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TOWARDS RELIABLE DATA-BASED OPTIMAL AND PREDICTIVE CONTROL USING EXTENDED DMD

arXiv

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arXiv 2022年

作者： Schaller, Manuel Worthmann, Karl Philipp, Friedrich Peitz, Sebastian Nüske, Feliks Technische Universität Ilmemau Institute of Mathematics Optimization-based Control Group Germany Paderborn University Department of Computer Science Data Science for Engineering Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany

While Koopman-based techniques like extended Dynamic Mode Decomposition are nowadays ubiquitous in the data-driven approximation of dynamical systems, quantitative error estimates were only recently established. To this end, both sources of error resulting from a finite dictionary and only finitely-many data points in the generation of the surrogate model have to be taken into account. We generalize the rigorous analysis of the approximation error to the control setting while simultaneously reducing the impact of the curse of dimensionality by using a recently proposed bilinear approach. In particular, we establish uniform bounds on the approximation error of state-dependent quantities like constraints or a performance index enabling data-based optimal and predictive control with guarantees. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamic mode decomposition

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RCA-IDS: A Novel Real-time Cloud-based Adversarial IDS for Connected Vehicles

RCA-IDS: A Novel Real-time Cloud-based Adversarial IDS for C...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Zahra Pooranian Mohammad Shojafar Pedram Asef Matthew Robinson Harry Lees Mark Longden Department of Computer Science University of Reading UK 5G/6GIC Institute for Communication Systems (ICS) University of Surrey Guildford UK Dept. of Mechanical Engineering University College London London UK Dept. of Engineering and Technology University of Hertfordshire Hatfield UK R&D Group RL Auto Ltd Basingstoke UK

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

This paper focuses on the requirement for creating novel frameworks to monitor and identify cyberattacks in Connected Vehicles (CVs). The health of the sensors in CVs becomes crucial when performance predictions and communication-related errors can compromise the resilience of the sensory network. To meet the evolving demands of connected vehicle (CV) systems, Intrusion Detection systems (IDS) must be regularly updated and tailored as powerful monitoring entities. To equip cloud-tied operators with the ability to comprehend unusual sensor data originating from vehicles at the cloud level, we designed an innovative Real-time Cloud-based Adversarial IDS called RCA-IDS. This system exclusively focuses on detecting and explaining instances of sensor data manipulation caused by poisoning attacks. Two attack mechanisms were created utilizing random-based and silhouette-based clustering methods. Subsequently, two defence mechanisms based on multi-layer neural network-type deep learning were proposed to counter these attacks. The newly introduced RCA-IDS demonstrates a minimum accuracy of 90% in detecting cyberattacks.

关键词： Deep learning Cloud computing Connected vehicles Clustering algorithms Real-time systems Liquid crystal displays Classification algorithms

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Reinforcement learning with non-ergodic reward increments: robustness via ergodicity transformations

arXiv

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arXiv 2023年

作者： Baumann, Dominik Noorani, Erfaun Price, James Peters, Ole Connaughton, Colm Schön, Thomas B. Cyber-physical Systems Group Aalto University Espoo Finland Department of Electrical and Computer Engineering University of Maryland College ParkMD United States Department of Mathematics University of Warwick Warwick United Kingdom London Mathematical Laboratory London United Kingdom Department of Information Technology Uppsala University Uppsala Sweden

Envisioned application areas for reinforcement learning (RL) include autonomous driving, precision agriculture, and finance, which all require RL agents to make decisions in the real world. A significant challenge hindering the adoption of RL methods in these domains is the non-robustness of conventional algorithms. In particular, the focus of RL is typically on the expected value of the return. The expected value is the average over the statistical ensemble of infinitely many trajectories, which can be uninformative about the performance of the average individual. For instance, when we have a heavy-tailed return distribution, the ensemble average can be dominated by rare extreme events. Consequently, optimizing the expected value can lead to policies that yield exceptionally high returns with a probability that approaches zero but almost surely result in catastrophic outcomes in single long trajectories. In this paper, we develop an algorithm that lets RL agents optimize the long-term performance of individual trajectories. The algorithm enables the agents to learn robust policies, which we show in an instructive example with a heavy-tailed return distribution and standard RL benchmarks. The key element of the algorithm is a transformation that we learn from data. This transformation turns the time series of collected returns into one for whose increments expected value and the average over a long trajectory coincide. Optimizing these increments results in robust policies. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

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Swarm-Intelligence Algorithms for Mining Numerical Association Rules: An Exhaustive Multi-Aspect Analysis of Performance Assessment Data

SSRN

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SSRN 2023年

作者： Kaushik, Minakshi Kõiva, Pilleriin Sharma, Rahul Fister, Iztok Draheim, Dirk Information Systems Group Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia School of Information Technologies Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia Faculty of Electrical Engineering and Computer Science University of Maribor Koroska cesta 46 Maribor2000 Slovenia

Numerical association rule mining (NARM) is an extended version of association rule mining that determines association rules in numerical data items, primarily via distribution, discretization and optimization techniques. Under the umbrella of optimization techniques, several evolutionary and swarm intelligence-based algorithms have been proposed to extract association rules from a numeric dataset. However, a sufficient understanding of the performance of swarm intelligence-based algorithms, especially for NARM, is still missing. In state-of-the-art, various swarm intelligence-based optimization algorithms are claimed to be better based on their arbitrary comparisons with different algorithms in different classes, e.g., swarm intelligence-based algorithms are compared with genetic algorithms. Unfortunately, they are not compared within their own class algorithms. Therefore, it is challenging to select an appropriate swarm intelligence-based algorithm for NARM. This article aims at filling this gap by conducting an exhaustive multi-aspect analysis of four popular swarm intelligence-based optimization algorithms (MOPAR, MOCANAR, ACO-R and MOB-ARM) with four real-world datasets and six major metrics and objectives: performance time, the number of rules, support, confidence, comprehensibility, and interestingness. In our analysis, the MOPAR algorithm produces a low number of rules and shows high values of confidence, comprehensibility, and interestingness. The MOCANAR algorithm provides satisfactory results with respect to all six parameters across all the data sets. The ACO-R algorithm produces high-quality rules but needs parameter modification for a large number of attributes in datasets, and the MOB-ARM algorithm is way slower than the other three algorithms. © 2023, The Authors. All rights reserved.

关键词： Association rules

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Magneto-oscillatory localization for small-scale robots

arXiv

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arXiv 2024年

作者： Fischer, Felix Gletter, Christian Jeong, Moonkwang Qiu, Tian Site Dresden Dresden Germany Faculty of Engineering Sciences University of Heidelberg Heidelberg Germany Cyber Valley Group – Biomedical Microsystems Institute of Physical Chemistry University of Stuttgart Stuttgart Germany Micro Nano and Molecular Systems Lab. Max Planck Institute for Medical Research Heidelberg Germany Faculty of Medicine Carl Gustav Carus Dresden University of Technology Dresden Germany Faculty of Electrical and Computer Engineering Dresden University of Technology Dresden Germany

Magnetism is widely used for the wireless localization and actuation of robots and devices for medical procedures. However, current static magnetic localization methods suffer from large required magnets and are limited to only five degrees of freedom due to a fundamental constraint of the rotational symmetry around the magnetic axis. We present the small-scale magneto-oscillatory localization (SMOL) method, which is capable of wirelessly localizing a millimeter-scale tracker with full six degrees of freedom in deep biological tissues. The SMOL device uses the temporal oscillation of a mechanically resonant cantilever with a magnetic dipole to break the rotational symmetry, and exploits the frequency-response to achieve a high signal-to-noise ratio with sub-millimeter accuracy over a large distance of up to 12 centimeters and quasi-continuous refresh rates up to 200 Hz. Integration into real-time closed-loop controlled robots and minimally-invasive surgical tools are demonstrated to reveal the vast potential of the SMOL method. © 2024, CC BY.

关键词： Surgical equipment

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STAR-RIS Assisted Cell-Free Massive MIMO System Under Spatially-Correlated Channels

arXiv

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arXiv 2023年

作者： Papazafeiropoulos, Anastasios Ngo, Hien Quoc Kourtessis, Pandelis Chatzinotas, Symeon The Communications and Intelligent Systems Research Group University of Hertfordshire HatfieldAL10 9AB United Kingdom SnT The University of Luxembourg Luxembourg The School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast BelfastBT7 1NN United Kingdom

This paper investigates the performance of downlink simultaneous transmitting and reflecting reconfigurable intelligent surface (STAR-RIS)-assisted cell-free (CF) massive multiple-input multiple-output (mMIMO) systems, where user equipments (UEs) are located on both sides of the RIS. We account for correlated Rayleigh fading and multiple antennas per access point (AP), while the maximum ratio (MR) beamforming is applied for the design of the active beamforming in terms of instantaneous channel state information (CSI). Firstly, we rely on an aggregated channel estimation approach that reduces the overhead required for channel estimation while providing sufficient information for data processing. We obtain the normalized mean square error (NMSE) of the channel estimate per AP, and design the passive beamforming (PB) of the surface based on the longtime statistical CSI. Next, we derive the received signal in the asymptotic regime of numbers of APs and surface elements. Then, we obtain a closed-form expression of the downlink achievable rate for arbitrary numbers of APs and STAR-RIS elements under statistical CSI. Finally, based on the derived expressions, the numerical results show the feasibility and the advantages of deploying a STAR-RIS into conventional CF mMIMO systems. In particular, we theoretically analyze the properties of STAR-RIS-assisted CF mMIMO systems and reveal explicit insights in terms of the impact of channel correlation, the number of surface elements, and the pilot contamination on the achievable rate. © 2023, CC BY.

关键词： MIMO systems

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Distributed coordination protocol for event data exchange in IoT monitoring applications 11

Distributed coordination protocol for event data exchange in...

引用

11th International Conference on Information and Knowledge Technology, IKT 2020

作者： Khazael, Behnam Malazi, Hadi Tabatabaee G.C. Shahid Beheshti University Faculty of Computer Science and Engineering Tehran Iran Distributed Systems Group SCSS Trinity College Dublin Dublin Ireland

ISBN: (纸本)9781665404419

Publish-subscribe architecture is one of the dominating architectural styles in designing the Internet of things (IoT) monitoring applications. Establishment of a brokerless publish-subscribe system leads to exchanging a high number of control messages including advertise, subscribe and update messages. Additionally, the in-efficiency in forwarding publish messages in a resource-constrained IoT environment results in network congestion, communication delay, and energy loss. The recent approaches in brokerless publish-subscribe systems for IoT domain introduced new communication protocols, maintaining routing tables of IoT nodes, and clustering the network. However, the efficiency in exchanging control messages does not receive much attention. In this paper, we propose an enhanced communication protocol to establish a brokerless publish-subscribe IoT system. The protocol is based on beaconing for packet dissemination. We applied a new structure that provides metadata in the packet header that facilitates the receivers regarding their next possible actions. The simulation results demonstrate that the proposed method reduces the network traffic by 28% on average and decreases the energy consumption of nodes up to 33%. The results also reveal that the number of publish messages is reduced by 10% in comparison to the baseline method. © 2020 IEEE.

关键词： Internet of things

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Image Classification with Deep Reinforcement Active Learning

arXiv

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arXiv 2024年

作者： Jiu, Mingyuan Song, Xuguang Sahbi, Hichem Li, Shupan Chen, Yan Guo, Wei Guo, Lihua Xu, Mingliang School of Computer and Artificial Intelligence Zhengzhou University China Engineering Research Center of Intelligent Swarm Systems Ministry of Education China National Supercomputing Center Zhengzhou China Sorbonne University CNRS LIP6 ParisF-75005 France Luoyang float glass group Co. Ltd State Key Laboratory of Advanced Technology for Float Glass China

Deep learning is currently reaching outstanding performances on different tasks, including image classification, especially when using large neural networks. The success of these models is tributary to the availability of large collections of labeled training data. In many real-world scenarios, labeled data are scarce, and their hand-labeling is time, effort and cost demanding. Active learning is an alternative paradigm that mitigates the effort in hand-labeling data, where only a small fraction is iteratively selected from a large pool of unlabeled data, and annotated by an expert (a.k.a oracle), and eventually used to update the learning models. However, existing active learning solutions are dependent on handcrafted strategies that may fail in highly variable learning environments (datasets, scenarios, etc). In this work, we devise an adaptive active learning method based on Markov Decision Process (MDP). Our framework leverages deep reinforcement learning and active learning together with a Deep Deterministic Policy Gradient (DDPG) in order to dynamically adapt sample selection strategies to the oracle’s feedback and the learning environment. Extensive experiments conducted on three different image classification benchmarks show superior performances against several existing active learning strategies. Copyright © 2024, The Authors. All rights reserved.

关键词： Deep reinforcement learning

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Estimation of Co2 Storage Capacities in Saline Aquifers Using Material Balance

SSRN

引用

SSRN 2024年

作者： Oh, Hyunmin Yoon, Hyunjee Park, Sangkeon Kim, Yeongju Choi, Byungin Sun, Wenyue Jeong, Hoonyoung Department of Energy Systems Engineering Seoul National University 1 Gwanakro Gwanak-gu Seoul08826 Korea Republic of Computer Modelling Group Middle East Ltd. Dubai500 446 United Arab Emirates Qingdao266580 China Research Institute of Energy and Resources Seoul National University 1 Gwanakro Gwanak-gu Seoul08826 Korea Republic of

Estimating CO2 storage capacities is crucial in developing carbon capture and storage projects. Material balance equation (MBE) methods, widely employed for oil and gas reserve estimation, offer a direct approach to estimating CO2 storage capacities. However, previous MBE methods rely on an original fluid in-place volume calculated using volumetric methods to estimate CO2 storage capacities, lacking validation for accuracy. It is essential to accurately estimate the original fluid in-place volume, representing the pore volume, as it substantially influences CO2 storage capacity. This study presents a refined MBE method that ensures accurate estimates of CO2 storage capacities by validating the original fluid in-place volumes in saline aquifers. The accuracy of this method was evaluated by comparing it with a commercial reservoir simulator for a synthetic aquifer example and the Sleipner L9 model. In the synthetic aquifer example, the relative error in CO2 storage capacity estimation with the proposed MBE method was only 2.09%, even when short-term (1-year) injection data were utilized. The proposed MBE method demonstrates consistent accuracy in estimating CO2 storage capacities under different aquifer properties, operating conditions, and MBE-related conditions. The proposed MBE method also accurately estimated the CO2 storage capacity in the Sleipner L9 model, achieving a relative error of 1.48%. © 2024, The Authors. All rights reserved.

关键词： Carbon capture

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