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Iranian Conference on Renewable Energy and Distributed Generation (ICREDG)

作者： Mostafa Riahinasab Ehsan Heydarian-Forushani Mehdi Baharizadeh Mohammad Hosein Ershadi Department of Electrical Engineering Khomeinishahr Branch Islamic Azad University Isfahan Iran Department of Electrical and Computer Engineering Qom University of Technology Qom Iran

ISBN: (数字)9798331525132

ISBN: (纸本)9798331525149

This paper proposes a cooperative energy management for smart distribution systems consisting of microgrids. The proposed model utilizes a centralized approach to manage the daily scheduling of microgrids in order to minimize their total costs. Microgrids integrate the battery energy storage system to control the probabilistic behavior of renewable generation. Also, real-time price-based demand response programs are considered to best model the flexibility of microgrids. The microgrids form a big alliance to reduce the complexity of the optimization problem and reduce the total cost of microgrids. Compared to the decentralized model, the proposed model reduces operation cost and unsupplied energy by 5.46 % and 100 %, respectively.

关键词： Renewable energy sources Costs Electricity Microgrids Optimal scheduling Probabilistic logic Demand response Distributed power generation Energy management Load modeling

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CO2 sensor placement for the prompt identification of high virus transmission-risk conditions 17

CO2 sensor placement for the prompt identification of high v...

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17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022

作者： Athanasiou, Modestos Kyriacou, Alexis Michaelides, Michalis P. Lelantus Innovations Ltd Nicosia Cyprus Department of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

Poor Indoor Air Quality (IAQ) can contribute to airborne viruses remaining viable for longer time periods with the increased risk of infecting more people. Various methodologies, usually by considering CO2 concentrations and well-mixed zones, have been proposed for the indirect assessment of the virus transmission risk. However, IAQ conditions can vary significantly within a room and the position of sensors is crucial to ensure real-time estimation of the virus transmission risk. In this work, a novel sensor placement methodology is presented for the prompt identification of increased virus transmission risk conditions, through the early detection of increased CO2 concentrations in the exhaled air. Specifically, the 3D airflow vectors for multiple ventilation scenarios are used to compute the impact of each possible sensor placement, and the optimal sensor positions are computed using multi-objective optimization. Simulation results are provided for calculating the optimal positions for 1-3 CO2 sensors in a real office-room. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

关键词： Multiobjective optimization

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A Novel Precoder for Peak-to-Average Power Ratio Reduction in OTFS Systems

arXiv

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arXiv 2025年

作者： Prakash, Saurabh Khammammetti, Venkatesh Mohammed, Saif Khan Department of Electrical Engineering Indian Institute of Technology Delhi India IIT Delhi India Department of Electrical and Computer Engineering Duke University United States

We consider the issue of high peak-to-average-power ratio (PAPR) of Orthogonal time frequency space (OTFS) modulated signals. This paper proposes a low-complexity novel iterative PAPR reduction method which achieves a PAPR reduction of roughly 5 dB when compared to a OTFS modulated signal without any PAPR compensation. Simulations reveal that the PAPR achieved by the proposed method is significantly better than that achieved by other state-of-art methods. Simulations also reveal that the error rate performance of OTFS based systems with the proposed PAPR reduction is similar to that achieved with the other state-of-art methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Signal modulation

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A Totally Asynchronous Nesterov's Accelerated Gradient Method for Convex Optimization 63

A Totally Asynchronous Nesterov's Accelerated Gradient Metho...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Pond, Ellie Sebok, April Bell, Zachary Hale, Matthew Georgia Institute of Technology School of Electrical and Computer Engineering AtlantaGA30332 United States University of Florida Department of Mechanical and Aerospace Engineering GainesvilleFL32611 United States AFRL/RW at Eglin Afb United States

ISBN: (纸本)9798350316339

We present a totally asynchronous algorithm for convex optimization that is based on a novel generalization of Nesterov's accelerated gradient method. This algorithm is developed for fast convergence under 'total asynchrony' which allows arbitrarily long delays between agents' computations and communications. These conditions may arise, for example, due to jamming by adversaries. Our framework is block-based, in the sense that each agent is only responsible for computing and communicating updates to a small subset of the network-level decision variables. In our main result, we present bounds on the algorithm's parameters that guarantee linear convergence to an optimizer. Then, we quantify the relationship between (i) the total number of computations and communications executed by the agents and (ii) the agents' collective distance to an optimum. Numerical simulations show that this algorithm requires 28% fewer iterations than the heavy ball algorithm and 61% fewer iterations than gradient descent under total asynchrony. © 2024 IEEE.

关键词： Convex optimization

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Mobility-Aware Service Migration for Seamless Provision: A Reinforcement Learning Approach

Mobility-Aware Service Migration for Seamless Provision: A R...

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2022 IEEE International Conference on Communications, ICC 2022

作者： Miao, Yuan Lyu, Feng Wu, Fan Wu, Huaqing Ren, Ju Zhang, Yaoxue Shen, Xuemin Sherman Central South University School of Computer Science and Engineering Changsha China Tsinghua University Department of Computer Science and Technology Beijing China University of Waterloo Department of Electrical and Computer Engineering WaterlooON Canada

ISBN: (数字)9781538683477

ISBN: (纸本)9781538683477

Mobile Edge Computing (MEC) is a promising paradigm to support high-quality time-sensitive applications. In this paper, we investigate the service migration (i.e., whether, when, and where to migrate the services) to seamlessly serve mobile users in small-cell MEC systems. The service migration is formulated as an optimization problem to minimize the long-term system average delay that consists of queuing, communication, and migration delays. Considering the dynamic user mobility and network conditions, the formulated problem is non-convex and difficult to solve in real time. To this end, we propose a Mobility-aware Service Migration scheme, named MSM, to make real-time decisions on service migrations by utilizing reinforcement learning (RL) approaches. Specifically, we first design a user classification mechanism based on users' mobility patterns to reduce the complexity of decision-making. We then formulate the service migration as a Markov decision process and devise an RL-based framework to make service migration decisions in real time in the dynamic MEC environment. Extensive data-driven experiments demonstrate the efficacy of MSM in reducing the system average delay. © 2022 IEEE.

关键词： Reinforcement learning

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Performance Analysis of Random Forest Classifier in Extracting Features from the EEG signal

Performance Analysis of Random Forest Classifier in Extracti...

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2022 International Conference on Advanced Computing Technologies and Applications, ICACTA 2022

作者： Jamunadevi, C. Ragupathy, P. Sritha, P. Pandikumar, S. Deepa, S. Kongu Engineering College Department of Computer Technology-PG Erode India Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Chennai India Bannari Amman Institute of Technology Department of Electrical and Electronics Engineering Sathyamangalam India American College Department of Computer Science Madurai India

ISBN: (纸本)9781665495158

Epilepsy is a disorder and is identified by baseless seizures that have been associated with unexpected improper neural discharges which result in various health issues and also result in death. One of the most common methods in monitoring and detecting contraction seizures is an electroencephalogram. But it is highly affordable and requires increased temporal resolution. EEG (electroencephalogram) is a commonly used method for monitoring and detecting seizures. The prevalence of EEG seizure detection has increased due to the increasing number of researchers who are focused on developing automated methods to detect the abnormalities in the EEG signals. But, it requires higher temporal resolution and is typically only available for a limited amount of time. Through machine learning, it is possible to extract the details of EEG signals that can help detect seizures. In this paper, the performance analysis is performed under various classifiers such as Random Forest, Gaussian Boosting, and AdaBoost. The results show that Random Forest is the most accurate classifier for achieving high degree of accuracy. © 2022 IEEE.

关键词： Electroencephalography

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Effectiveness of Almost Sub Frame Approach as Inter Cell Interference Management in LTE-A Macro – Pico Scenario

Effectiveness of Almost Sub Frame Approach as Inter Cell Int...

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International Conference for Convergence of technology (I2CT)

作者： Kehali A. Jember Tadele A. Abose Ermias Kassahun T. Venu Madhav Department Electronics and Communication Engineering Adama Science and Technology University Adama Ethiopia Department Electrical and Computer Engineering Mettu University Ethiopia Department Electrical and Computer Engineering Gondar University Gondar Ethiopia

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

Heterogeneous networks are promising solutions for enhancing network performance of LTE-A mobile networks by deploying small cells within the area of the serving macro cells. The goal of deploying such networks is to offload data from the congested macro cells towards the small cells. However, cross tier interference is a serious issue, which is introduced due to the transmitted power difference between Macro cell node and small cell node. Hence, cell age users are mainly affected by high power interference originated from Macro node. The paper develops enhanced inter-cell interference coordination using Almost Blank Sub-Frame (ABS) to improve user throughput. This can be achieved by stopping data transmission on in certain sub frame of macro cell while Pico users are scheduled at the same frame. computer simulation results depict that the performance of Pico cell users is highly degraded, especially for cell edge users. The ABS technique improves cell edge user throughput of Pico cell by 95% and center cell user throughput by 75.25%.

关键词： computer simulation Interference Throughput Heterogeneous networks Data communication Intercell interference Long Term Evolution

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Harnessing ML and NLP for Enhanced Cybersecurity: A Comprehensive Approach for Phishing Email Detection 25

Harnessing ML and NLP for Enhanced Cybersecurity: A Comprehe...

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25th International Arab Conference on Information technology, ACIT 2024

作者： Aljamal, Mahmoud Alquran, Rabee Aljaidi, Mohammad Aljamal, Osama S Alsarhan, Ayoub Al-Aiash, Issa Samara, Ghassan Banisalman, Mohammad Khouj, Mohammad Irbid National University Faculty of Science and Information Technology Department of Cybersecurity Irbid21110 Jordan The Hashemite University Faculty of Prince Al-Hussien bin Abdullah for It Department of Information Technology Zarqa Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa13110 Jordan Al Hussein Technical University Department of Computer Science Amman Jordan Irbid Jordan Zarqa University Department of Computer Science Zarqa13110 Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia

ISBN: (纸本)9798331540012

Emails stand as a predominant mode of online communication, facilitating the exchange of information. With the ubiquity of emails, there has been a marked rise in unsolicited messages, particularly phishing emails. These deceptive emails aim to mislead recipients, often masquerading as trustworthy entities, to extract confidential information. Embedded with dubious offers, advertisements, malicious links, malware, and trojans, perpetrators deploy these emails with malicious intent, primarily to commit fraud. Consequently, distinguishing between phishing attempts and genuine emails becomes paramount. This study constructs machine learning-based models to detect phishing emails, ensuring accurate discernment between genuine and fraudulent messages. Leveraging two datasets, the Fraud Email Dataset and the Phishing Email Dataset, we developed and evaluated classifiers, including Logistic Regression, Decision Tree, Random Forest, and SVM. The Random Forest model emerged as the most effective, achieving accuracies of 98.72% on the Fraud Email Dataset and 99.15% on the Phishing Email Dataset, demonstrating strong generalizability across diverse phishing patterns. These results underscore the efficacy of ensemble methods for enhancing cybersecurity by reliably detecting phishing emails. © 2024 IEEE.

关键词： Phishing

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Data Visualization of Film Capacitors

Data Visualization of Film Capacitors

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2022 Tsinghua-IET electrical engineering Academic Forum, TDF 2022

作者： Zhang, Yongxin Chen, Fangyi Liu, Difan Feng, Qikun Zhong, Shaolong Pei, Jiayao Yang, Zhe Dang, Zhimin Department of Electrical Engineering Tsinghua University Beijing China Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh United States Department of Energy Technology Aalborg University Aalborg Denmark

ISBN: (数字)9781839537998

ISBN: (纸本)9781839537998

A large amount of data has been accumulated in the field of film capacitors, and these data resources need to be utilized to release the hidden value and provide a reference paradigm for subsequent researchers. Data visualization can open up a new path for the digital transformation of the film capacitor industry. Considering these, the characteristics of the collected film capacitor data are summarized. Next, the role of data visualization, fundamental forms and their respective prominent features of data visualization are combed and refined. Then, case studies on the three main data forms of film capacitors: text, digit, andimage are conducted to construct new visualization presentations by using the aforementioned fundamental forms consideringthe specific data characteristics. The research results show that the fundamental form of data visualization and the specificvisualization presentation constructed in this paper can help relevant practitioners efficiently and accurately grasp the laws andknowledge in film capacitor data, inspire researchers to discover hidden relationships, and provide a reference for the data utilization of generators, transformers, converters and other energy equipment in the energy internet. Finally, the challenges and the key issues that need to be addressed in future research have been prospected. © 2022 IET Conference Proceedings. All rights reserved.

关键词： Data visualization

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A Comprehensive Exploration of Renewable Energy Education Landscape in Ethiopian Public Universities

A Comprehensive Exploration of Renewable Energy Education La...

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2024 IEEE PES/IAS PowerAfrica, PowerAfrica 2024

作者： Temesgen, Adugnaw Lake Bekele, Getachew Broto, Vanesa Castan Mulugeta, Yacob School of Electrical and Computer Engineering Addis Ababa University Addis Ababa Ethiopia Urban Insctitute University of Sheffield Sheffield United Kingdom University College London Department of Science Technology Engineering and Public Policy London United Kingdom

ISBN: (纸本)9798350389388

The renewable energy sector has experienced a rapid expansion, driven by rising fossil fuel costs, increasing concerns about energy security, and the imperative to electrify rural areas. This growth creates a demand for skilled professionals, and higher education institutions must respond by providing comprehensive and practical renewable energy education. This study aims to analyze energy education in Ethiopian public universities, considering program offerings, content, geographical distribution, challenges, and opportunities. To achieve this, a multifaceted methodology involving systematic curriculum reviews, targeted surveys, data compilation, and interactive spatial mapping is employed. Findings indicate a diverse landscape, with both standalone programs and integrated courses available. Among the 45 public universities, only 22% of them have Institutes of Technologies (IoTs), indicating a significant technology-focused institute gap. None of these universities provide standalone energy programs at the BSc level, and less than 10% offer such programs at the MSc level. Notably, key energy courses like 'Energy Conversion and Rural Electrification' and 'Hydropower engineering' have been identified, taught in 78% of public universities, and integrated within electrical Power engineering programs at the BSc level. However, the evident lack of practical training and experiential learning in energy education has implications for the sector's development in Ethiopia. © 2024 IEEE.

关键词： Clean energy

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