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arXiv 2022年

作者： Behera, Sadananda Panayiotou, Tania Ellinas, Georgios KIOS Research Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

In this work, the capabilities of an encoder-decoder learning framework are leveraged to predict soft-failure evolution over a long future horizon. This enables the triggering of timely repair actions with low quality-of-transmission (QoT) margins before a costly hard-failure occurs, ultimately reducing the frequency of repair actions and associated operational expenses. Specifically, it is shown that the proposed scheme is capable of triggering a repair action several days prior to the expected day of a hard-failure, contrary to soft-failure detection schemes utilizing rule-based fixed QoT margins, that may lead either to premature repair actions (i.e., several months before the event of a hard-failure) or to repair actions that are taken too late (i.e., after the hard failure has occurred). Both frameworks are evaluated and compared for a lightpath established in an elastic optical network, where soft-failure evolution can be modeled by analyzing bit-error-rate information monitored at the coherent receivers. Copyright © 2022, The Authors. All rights reserved.

关键词： Machine learning

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Adaptive responses of cancer cells to tumor microenvironment changes: an evolutionary model perspective

Adaptive responses of cancer cells to tumor microenvironment...

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IEEE International Conference on Automation Science and engineering (CASE)

作者： Chiara Romano Alessandro Borri Maria Domenica Di Benedetto Department of Information Engineering Computer Science and Mathematics Center of Excellence for Research DEWS University of L’Aquila Italy CNR-IASI Biomathematics Laboratory National Research Council of Italy Rome Italy

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

Evolution has always driven species change. The most interesting manifestations of evolution arise from natural selection, that favors the settling of more suitable species at the expense of others. This also occurs in cancer, where the hostile environment drives cancer cells to evolve to acquire favorable traits, phenotypes, in order to enlarge their species. A therapy can render the environment hostile, thus the physician’s role in limiting cell growth while considering their evolution is crucial. In this paper, we focus on the formalization and analysis of a novel eco-evolutionary model of cancer cells, enriched by biological considerations. Simulations validate the model and show how the role of the treatment is crucial in driving cellular dynamics.

关键词： Analytical models Adaptation models Evolution (biology) Biological system modeling Hypoxia Data models Biological cells Cancer Tumors Immune system

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Exploring Text-Guided Single Image Editing for Remote Sensing Images

arXiv

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arXiv 2024年

作者： Han, Fangzhou Si, Lingyu Dong, Hongwei Zhang, Lamei Chen, Hao Du, Bo The Department of Information Engineering Harbin Institute of Technology Harbin China The National Key Laboratory of Space Integrated Information System Institute of Software Chinese Academy of Sciences Beijing China The Hubei Luojia Laboratory National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan China

Artificial intelligence generative content (AIGC) has significantly impacted image generation in the field of remote sensing. However, the equally important area of remote sensing image (RSI) editing has not received sufficient attention. Deep learning based editing methods generally involve two sequential stages: generation and editing. During the generation stage, consistency in content and details between the original and edited images must be maintained, while in the editing stage, controllability and accuracy of the edits should be ensured. For natural images, these challenges can be tackled by training generative backbones on large-scale benchmark datasets and using text guidance based on vision-language models (VLMs). However, these previously effective approaches become less viable for RSIs due to two reasons: First, existing generative RSI benchmark datasets do not fully capture the diversity of remote sensing scenarios, particularly in terms of variations in sensors, object types, and resolutions. Consequently, the generalization capacity of the trained backbone model is often inadequate for universal editing tasks on RSIs. Second, the large spatial resolution of RSIs exacerbates the problem in VLMs where a single text semantic corresponds to multiple image semantics, leading to the introduction of incorrect semantics when using text to guide RSI editing. To solve above problems, this paper proposes a text-guided RSI editing method that is controllable but stable, and can be trained using only a single image. It adopts a multi-scale training approach to preserve consistency without the need for training on extensive benchmark datasets, while leveraging RSI pre-trained VLMs and prompt ensembling (PE) to ensure accuracy and controllability in the text-guided editing process. High-quality RSI editing results can be achieved by first applying multi-scale training to the target image, followed by text-guided fine-tuning using RSI pre-trained VLMs and PE. Experi

关键词： Semantics

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Smart Energy Management System Using Machine Learning

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computers, Materials & Continua 2024年第1期78卷 959-973页

作者： Ali Sheraz Akram Sagheer Abbas Muhammad Adnan Khan Atifa Athar Taher M.Ghazal Hussam Al Hamadi School of Computer Science National College of Business Administration&EconomicsLahore54000Pakistan Riphah School of Computing&Innovation Faculty of ComputingRiphah International UniversityLahore CampusLahore54000Pakistan Pattern Recognition and Machine Learning Lab Department of SoftwareGachon UniversitySeongnamGyeonggido13120Korea Department of Computer Science COMSATS University IslamabadLahore CampusLahore54000Pakistan School of Information Technology Skyline University CollegeSharjah1797United Arab Emirates Network and Communication Technology Lab Center for Cyber SecurityFaculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia College of Engineering and IT University of DubaiDubaiUnited Arab Emirates

Energy management is an inspiring domain in developing of renewable energy ***,the growth of decentralized energy production is revealing an increased complexity for power grid managers,inferring more quality and reliability to regulate electricity flows and less imbalance between electricity production and *** major objective of an energy management system is to achieve optimum energy procurement and utilization throughout the organization,minimize energy costs without affecting production,and minimize environmental *** energy management is an essential and complex subject because of the excessive consumption in residential buildings,which necessitates energy optimization and increased user *** address the issue of energy management,many researchers have developed various frameworks;while the objective of each framework was to sustain a balance between user comfort and energy consumption,this problem hasn’t been fully solved because of how difficult it is to solve *** inclusive and Intelligent Energy Management System(IEMS)aims to provide overall energy efficiency regarding increased power generation,increase flexibility,increase renewable generation systems,improve energy consumption,reduce carbon dioxide emissions,improve stability,and reduce energy *** Learning(ML)is an emerging approach that may be beneficial to predict energy efficiency in a better way with the assistance of the Internet of Energy(IoE)*** IoE network is playing a vital role in the energy sector for collecting effective data and usage,resulting in smart resource *** this research work,an IEMS is proposed for Smart Cities(SC)using the ML technique to better resolve the energy management *** proposed system minimized the energy consumption with its intelligent nature and provided better outcomes than the previous approaches in terms of 92.11% accuracy,and 7.89% miss-rate.

关键词： Intelligent energy management system smart cities machine learning

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Stackelberg evolutionary games for cancer modeling and treatment

Stackelberg evolutionary games for cancer modeling and treat...

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IEEE Conference on Decision and Control

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Stackelberg Evolutionary Game (SEG) theory models the interacting dynamics between a rational leader and a population of evolving followers, merging classical and evolutionary game theory. Although both methods are welldeveloped individually, the potential of SEG itself has not been appropriately recognized. Thus, in this paper we propose a novel eco-evolutionary model with resource mutualism and competition and we introduce a control framework based on SEGs, to steer the eco-evolutionary dynamics of followers at will. As a case study, we consider the treatment of cancer, where tumour cells are the followers that evolve in response to changes in the tumour micro-environment (and thus on available resources) and to the medical therapy, where the physician is the leader. An interesting aspect of this approach is that the objective function can be tailored according to the goal, e.g. jointly balancing tumour size, developed resistance, and toxicity of therapy, to ensure maximum quality of life for the patient. Simulations confirm the effectiveness and the potential of the proposed approach.

关键词： Translation Toxicology Biological system modeling Cancer treatment Games Numerical models Tumors Cancer Context modeling Immune system

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Modeling Soft-Failure Evolution for Triggering Timely Repair with Low QoT Margins

Modeling Soft-Failure Evolution for Triggering Timely Repair...

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Sadananda Behera Tania Panayiotou Georgios Ellinas Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (纸本)9781665435413

关键词： Location awareness Time-frequency analysis Uncertainty Network topology Receivers Maintenance engineering Predictive models

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Towards a Fully Autonomous UAV Controller for Moving Platform Detection and Landing

Towards a Fully Autonomous UAV Controller for Moving Platfor...

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International Conference on VLSI Design

作者： Michalis Piponidis Panayiotis Aristodemou Theocharis Theocharides Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus Nicosia Cyprus

ISBN: (数字)9781665485050

ISBN: (纸本)9781665496612

While Unmanned Aerial Vehicles (UAVs) are increasingly deployed in several missions, their inability of reliable and consistent autonomous landing poses a major setback for deploying such systems truly autonomously. In this paper we present an autonomous UAV landing system for landing on a moving platform. In contrast to existing attempts, the proposed system relies only on the camera sensor, and has been designed as lightweight as possible. The proposed system can be deployed on a low power platform as part of the drone payload, whilst being indifferent to any external communication or any other sensors. The system relies on a Neural Network (NN) based controller, for which a target and environment agnostic simulator was created, used in training and testing of the proposed system, via Reinforcement Learning (RL) and Proximal Policy optimization (PPO) to optimally control and steer the drone towards landing on the target. Through real-world testing, the system was evaluated with an average deviation of 15cm from the center of the target, for 40 landing attempts.

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Privacy of distributed optimality schemes in power networks

arXiv

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arXiv 2022年

作者： Khan, Kanwal Kasis, Andreas Polycarpou, Marios M. Timotheou, Stelios The KIOS Research Innovation Center of Excellence The Department of Electrical and Computer Engineering University of Cyprus Cyprus Cyprus

The increasing participation of local generation and controllable demand units within the power network motivates the use of distributed schemes for their control. Simultaneously, it raises two issues;achieving an optimal power allocation among these units, and securing the privacy of the generation/demand profiles. This study considers the problem of designing distributed optimality schemes that preserve the privacy of the generation and controllable demand units within the secondary frequency control timeframe. We propose a consensus scheme that includes the generation/demand profiles within its dynamics, keeping this information private when knowledge of its internal dynamics is not available. However, the prosumption profiles may be inferred using knowledge of its internal model. We resolve this by proposing a privacy-preserving scheme which ensures that the generation/demand cannot be inferred from the communicated signals. For both proposed schemes, we provide analytic stability, optimality and privacy guarantees and show that the secondary frequency control objectives are satisfied. The presented schemes are distributed, locally verifiable and applicable to arbitrary network topologies. Our analytic results are verified with simulations on a 140-bus system, where we demonstrate that the proposed schemes offer enhanced privacy properties, enable an optimal power allocation and preserve the stability of the power network. © 2022, CC BY-SA.

关键词： Electric network analysis

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A fast and effective maximum power point tracking method under various weather conditions for grid-connected photovoltaic systems

A fast and effective maximum power point tracking method und...

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IEEE Conference on Power Electronics and Renewable Energy (CPERE)

作者： Karam Khairullah Mohammed Saad Mekhilef Muhyaddin Rawa Control and Mechatronics Engineering Department School of Electrical Engineering Universiti Teknologi Malaysia Johor Bahru Malaysia School of Software and Electrical Engineering Faculty of Science Engineering and Technology Swinburne University of Technology Hawthorn VIC Australia Smart Grids Research Group Center of Research Excellence in Renewable Energy and power Systems King Abdulaziz University Jeddah Saudi Arabia Department Of Electrical and computer Engineering Faculty of engineering K. A Care Energy Research and Innovation Center King Abdulaziz University Jeddah Saudi Arabia

Photovoltaic systems (PV) are exposed to various weather conditions during the day and getting the maximum power of a PV system is one of its major challenges. While the conventional methods take time to track the maximum power accompanying the fluctuation around the maximum power. Consequently, several metaheuristic algorithms have been presented to overcome the fluctuating around the maximum power. On the other hand optimization methods will search into entire the PV curve to extract the maximum power. This leads to unnecessary search points during the uniform shading conditions which decrease the convergence speed towards the maximum power. In overcoming the tracking time issues as well as the capacity to distinguish between uniform and partial shading conditions, a quick and efficient tracking technique connected to the grid system was presented in this paper. The proposed system, has been tested using MATLAB/SIMULINK, which includes a boost converter to track the maximum power with a sampling time of 0.05 seconds, While a voltage source converter (VSC) is used to transfer the power generated by solar panels to the grid. The simulation results demonstrated that the proposed method was successfully implemented, with an average tracking time of 0.412 s for different weather conditions. Moreover, the comparison with the conventional method of perturb and observe (P& O) has been presented to evaluate the proposed method’s efficiency.

关键词： Maximum power point trackers Renewable energy sources Fluctuations Simulation Optimization methods Power electronics Solar panels

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Towards a Fully Autonomous UAV Controller for Moving Platform Detection and Landing

arXiv

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arXiv 2022年

作者： Piponidis, Michalis Aristodemou, Panayiotis Theocharides, Theocharis KIOS Research Innovation Center of Excellence Department of Electrical and Computer Engineering University of Cyprus Nicosia Cyprus

While Unmanned Aerial Vehicles (UAVs) are increasingly deployed in several missions, their inability of reliable and consistent autonomous landing poses a major setback for deploying such systems truly autonomously. In this paper we present an autonomous UAV landing system for landing on a moving platform. In contrast to existing attempts, the proposed system relies only on the camera sensor, and has been designed as lightweight as possible. The proposed system can be deployed on a low power platform as part of the drone payload, whilst being indifferent to any external communication or any other sensors. The system relies on a Neural Network (NN) based controller, for which a target and environment agnostic simulator was created, used in training and testing of the proposed system, via Reinforcement Learning (RL) and Proximal Policy Optimization (PPO) to optimally control and steer the drone towards landing on the target. Through real-world testing, the system was evaluated with an average deviation of 15cm from the center of the target, for 40 landing attempts. © 2022, CC BY.

关键词： Drones

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