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13th International Conference on Power, Energy and Electrical engineering, CPEEE 2023

作者： Alghobari, Mohammed Alhussainy, Abdullah Ali Alghamdi, Sultan Rawa, Muhyaddin Abusorrah, Abdullah King Abdulaziz University K. A. Care Energy Research and Innovation Center Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia King Abdulaziz University K. A. Care Energy Research and Innovation Center Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia King Abdulaziz University Smart Grids Research Group Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Jeddah21589 Saudi Arabia

ISBN: (纸本)9781665490146

This paper presents a robust secondary frequency control integrated with decoupled trigonometric saturated droop controller (DTSDC). The primary frequency controller may induce an error in frequency regulation, which affects the load sharing accuracy among microgrid distributed generators (DGs). Therefore, a centralized secondary controller is designed to converge the frequency to its nominal value for grid-forming inverters. Using an advanced communication channel, the proposed primary and secondary controller can primarily restore the frequency during disturbances, such as DG on/off switching and loading system variation. The practicality and performance of the proposed controller have been demonstrated with MATLAB simulation environment on a mesh microgrid system consisting of four parallel DGs-based inverters. Simulation results show the proposed controller's robustness in the microgrid frequency stability. Hence, it achieves accurate active power sharing with a linear load. Moreover, the impact of resistive and inductive line impedance is discussed in this study. A comparison between the microgrid centralized controller (MGCC) and the decoupled trigonometric saturated droop controller (DTSDC) only is conducted to show the robustness of the presented controller performance. © 2023 IEEE.

关键词： Distributed power generation

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Enhancing malware detection performance: leveraging K-Nearest Neighbors with Firefly Optimization Algorithm

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Multimedia Tools and Applications 2025年第12期84卷 10071-10094页

作者： Al Saaidah, Adeeb Abualhaj, Mosleh M. Shambour, Qusai Y. Abu-Shareha, Ahmad Adel Abualigah, Laith Al-Khatib, Sumaya N. Alraba’nah, Yousef H Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Software Engineering Al-Ahliyya Amman University Amman Jordan Department of Data Science and Artificial Intelligence Al-Ahliyya Amman University Amman Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan Research Center University of Tabuk Tabuk71491 Saudi Arabia MEU Research Unit Middle East University Amman11831 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon College of Engineering Yuan Ze University Taoyuan32003 Taiwan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia

Malware detection plays a crucial role in ensuring robust cybersecurity amidst the ever-evolving cyber threats. This research paper delves into the realm of machine learning (ML) algorithms for malware detection, with a specific emphasis on the K-Nearest Neighbors (KNN) algorithm, utilizing tailored parameter settings and the Firefly Optimization Algorithm (FOA). The study leverages the MalMem-2022 dataset to assess the efficacy of KNN and KNN with FOA in malware detection. The impact of parameter tuning and feature selection on classification is elucidated by comparing the performance of both approaches. Encouragingly, the results reveal promising advancements in one of the multiclass classification scenarios when employing KNN with FOA, yielding noteworthy enhancements in Accuracy, Recall, Precision, Matthews Correlation Coefficient, TNR, and F1-score by 2.65%, 2.65%, 2.47%, 3.59%, 0.17%, and 2.61%, respectively. These findings accentuate the significance of optimizing KNN’s parameters and implementing FOA for feature selection, culminating in heightened accuracy in malware detection. The symbiotic fusion of KNN and FOA exhibits remarkable effectiveness in augmenting the classification model’s performance, thus offering valuable insights for bolstering cybersecurity measures. Adopting ML algorithms, particularly KNN with specific parameter settings and FOA, presents a promising avenue for enhancing malware detection capabilities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Nearest neighbor search

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Disassembly Sequence Planning:A Survey

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IEEE/CAA Journal of Automatica Sinica 2021年第7期8卷 1308-1324页

作者： Xiwang Guo MengChu Zhou Abdullah Abusorrah Fahad Alsokhiry Khaled Sedraoui the College of Computer and Communication Engineering Liaoning Shihua UniversityFushun 113001China the Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewark NJ 07102 USA the Center of Research Excellence in Renewable Energy and Power Systems King Abdulaziz UniversityJeddah 21481Saudi Arabia the Department of Electrical and Computer Engineering Faculty of Engineeringand the Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

It is well-recognized that obsolete or discarded products can cause serious environmental pollution if they are poorly be *** contain reusable resource that can be recycled and used to generate desired economic ***,performing their efficient disassembly is highly important in green manufacturing and sustainable economic *** typical examples are electronic appliances and electromechanical/mechanical *** paper presents a survey on the state of the art of disassembly sequence *** can help new researchers or decision makers to search for the right solution for optimal disassembly *** reviews the disassembly theory and methods that are applied for the processing,repair,and maintenance of obsolete/discarded *** paper discusses the recent progress of disassembly sequencing planning in four major aspects:product disassembly modeling methods,mathematical programming methods,artificial intelligence methods,and uncertainty *** survey should stimulate readers to be engaged in the research,development and applications of disassembly and remanufacturing methodologies in the Industry 4.0 era.

关键词： Artificial intelligence(AI) disassembly sequence planning(DSP) intelligent optimization modeling methods multiobjective optimization single-objective optimization uncertainty.

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A Probabilistic Optimal UAV Trajectory Planning Approach to Minimise the Uncertainty of Traffic Density Estimations

A Probabilistic Optimal UAV Trajectory Planning Approach to ...

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International Conference on Intelligent Transportation

作者： Y. Englezou S. Timotheou C.G. Panayiotou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

Traffic monitoring has been one of the major tools used for transportation operations and planning. The emergence of Unmanned Aerial Vehicles (UAVs) provides new capabilities for traffic management purposes. One of the most challenging tasks of UAVs is path planning, that aims to determine the most efficient route of a UAV from an initial point to a target point, with respect to a specific task to be optimised. In this work we focus on the efficient traffic density estimation. Towards this, we propose an online probabilistic UAV trajectory construction methodology, that aims to select the next moves from which the UAV should obtain measurements of the traffic density, while aiming to minimise the total uncertainty of the traffic density across all time-space points in the time-horizon under study. We incorporate the Gaussian Process (GP) model to accurately estimate traffic density of specific road segments in a multi-lane highway even when data points are sparse within the specified time-space region under study. We employ a decision-theoretic methodology to develop Bayesian optimal UAV trajectories aimed at minimizing traffic density uncertainty. Finally, a simulation study is performed to evaluate the proposed methodology that shows that it reduces the uncertainty of the traffic density up to 70% compared to a simple cyclical UAV trajectory.

关键词： Uncertainty Trajectory planning Roads Estimation Transportation Autonomous aerial vehicles Probabilistic logic Trajectory Planning Monitoring

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Optimizing the Crossing Sequence in Autonomous Intersection Management with Travel Time and Energy Considerations

Optimizing the Crossing Sequence in Autonomous Intersection ...

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American Control Conference (ACC)

作者： Andreas Hadjigeorgiou Stelios Timotheou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

Connected and Automated Vehicles (CAVs) offer fine-grained control and connectivity with other vehicles and the surrounding infrastructure that enable safe and energy efficient navigation. In this paper, mathematical programming is employed to construct optimal CAV crossing sequences to minimize a combination of fuel consumption and travel time. The developed approach is based on the construction of the minimum energy profile for each CAV which allows the derivation of efficient crossing times, using Mixed Integer Programming, without the need to consider vehicle dynamics. Safety is then guaranteed by ensuring that the vehicles achieve the corresponding crossing times using Quadratic Programming. The effectiveness of the derived CAV sequences compared to FIFO is illustrated through extensive simulations. The results indicate that resequencing yields a minor reduction in travel time but a significant improvement in fuel consumption. Fuel consumption improves for increasing number of CAVs arriving at the intersection. CAV rescheduling further supports observations of previous works that small sacrifices in travel time result in considerable fuel savings.

关键词： Integer programming Navigation Energy efficiency Safety Dynamic programming Fuels Quadratic programming

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Enhancing Traffic State Estimation Using UAV-Based Measurements

Enhancing Traffic State Estimation Using UAV-Based Measureme...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Y. Englezou S. Timotheou C.G. Panayiotou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

Traffic state estimation is a challenging task due to the collection of sparse and noisy measurements from specific points of the traffic network. The emergence of Unmanned Aerial Vehicles (UAVs) provides new capabilities for traffic state estimation using density measurements at irregular time-points from all links of a given network under study. This work proposes a data-driven traffic density estimation method utilising measurements collected from a swarm of UAVs deployed over the network under study and no traffic models or historical data are required. A simulation study is conducted to compare the quality of information obtained from UAV-based measurements, to the information provided by other sensing technologies, particularly fixed-location sensors and Connected and Automated Vehicles (CAVs). Notably, while CAV-based and UAV-based sensing provide information with higher spatiotemporal resolutions compared to fixed-location sensors, UAV-based sensing exhibits higher estimation accuracy even under low penetration rates of UAVs flying above the network and low percentages of network coverage.

关键词： Accuracy Density measurement Probabilistic logic Time measurement Sensors Spatiotemporal phenomena Trajectory

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A Hybrid CNN-GRU model for Session-based Recommender Systems 9

A Hybrid CNN-GRU model for Session-based Recommender Systems

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9th International Conference on computer and Communications, ICCC 2023

作者： Tegene, Abebe Tamrat Liu, Qiao Gan, Yanglei Jimale, Elias Ayenew, Melak Leka, Habte University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China Addis Ababa Science and Technology University Center of Excellence of Ai and Robotics Addis Ababa Ethiopia University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu China

ISBN: (纸本)9798350317251

In many e-commerce recommendation systems, user profiles are mostly not available. In such cases, the recommendation task considers user behaviors like click sessions or browsing history. Traditional recommender methods often rely on users' past interactions for recommending user interests, which is difficult to apply to session-based recommender systems. However, these methods perform very poorly on this task. Recently, deep learning techniques such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) showed outstanding performance in session-based recommendation problems. In this paper, we propose a hybrid CNN and gated recurrent unit (GRU) model that exploits the advantages of both CNN and GRU in modeling the rigid order requirement for interactions inside sessions and modeling the sequential long-term dependencies in session data, respectively. Our proposed hybrid model has two architectures: the first part consists of the CNN layers that take input from Item ID to learn local features and relationships in sessions, and the second part is a GRU layer that takes the output from the CNN layer to learn the long-term dependencies in that session. This hybrid technique enables the architecture for modeling prominent features in the session to provide accurate recommendations. Extensive experiments over a real-world dataset demonstrate that the proposed hybrid model performs well and outperforms those GRU and CNN-based models in the state-of-the-art recommendation task on both metrics used. © 2023 IEEE.

关键词： Recommender systems

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A New Flow-Based Approach for Enhancing Botnet Detection Efficiency Using Convolutional Neural Networks and Long Short-Term Memory

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Knowledge and Information Systems 2025年 1-32页

作者： Asadi, Mehdi Heidari, Arash Jafari Navimipour, Nima Department of Computer Engineering Islamic Azad University Khameneh Branch Khameneh Iran Department of Software Engineering Haliç University Istanbul Turkey Department of Computer Engineering Faculty of Engineering and Natural Science İstanbul Atlas University Istanbul Turkey Department of Computer Science and Engineering Qatar University Doha Qatar Department of Computer Engineering Faculty of Engineering and Natural Sciences Kadir Has University Istanbul Turkey Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan Research Center of High Technologies and Innovative Engineering Western Caspian University Baku Azerbaijan

Despite the growing research and development of botnet detection tools, an ever-increasing spread of botnets and their victims is being witnessed. Due to the frequent adaptation of botnets to evolving responses offered by host-based and network-based detection mechanisms, traditional methods are found to lack adequate defense against botnet threats. In this regard, the suggestion is made to employ flow-based detection methods and conduct behavioral analysis of network traffic. To enhance the performance of these approaches, this paper proposes utilizing a hybrid deep learning method that combines convolutional neural network (CNN) and long short-term memory (LSTM) methods. CNN efficiently extracts spatial features from network traffic, such as patterns in flow characteristics, while LSTM captures temporal dependencies critical to detecting sequential patterns in botnet behaviors. Experimental results reveal the effectiveness of the proposed CNN-LSTM method in classifying botnet traffic. In comparison with the results obtained by the leading method on the identical dataset, the proposed approach showcased noteworthy enhancements, including a 0.61% increase in precision, a 0.03% augmentation in accuracy, a 0.42% enhancement in the recall, a 0.51% improvement in the F1-score, and a 0.10% reduction in the false-positive rate. Moreover, the utilization of the CNN-LSTM framework exhibited robust overall performance and notable expeditiousness in the realm of botnet traffic identification. Additionally, we conducted an evaluation concerning the impact of three widely recognized adversarial attacks on the Information Security Centre of excellence dataset and the Information Security and Object Technology dataset. The findings underscored the proposed method’s propensity for delivering a promising performance in the face of these adversarial challenges.

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A Robust Pilot Decontamination Scheme in Massive MIMO Systems: Integrating Rateless Orthogonal STBC, Weighted Graph Coloring, and Channel Estimation

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IEEE Access 2025年 13卷 94453-94463页

作者： Habib M. Hussien Zelalem A. Kelem Department of Electrical and Computer Engineering Artificial Intelligence and Robotics Center of Excellence Addis Ababa Science and Technology University Addis Ababa Ethiopia

Even though Multiple Input Multiple Output (MIMO) systems are one of the greatest innovations in wireless cellular network technology, they still face many challenges as we progress to 6G, hindering their ability to achieve the best throughput. In cellular networks, channel estimations are made using pilot sequences. Deploying these orthogonal time-frequency limited resources leads to pilot contamination, causing incorrect channel estimation due to mixed signals from neighboring cells and vice versa. Previous research has proven that this significantly decreases system performance. To reduce the impact and improve the output of Multiuser MIMO systems, this paper proposes a hybrid model combining Rateless Orthogonal Space-Time Block Codes (ROSTBC), Weighted Graph Coloring Pilot Allocation (WGCPA), and Maximum Likelihood Estimation (MLE). While ROSTBC has the ability to adapt to channel conditions, allowing the system to perform well despite pilot contamination, WGCPA and MLE address the problems of pilot contamination and the accuracy of channel estimation, respectively. The results showed that this hybrid scheme has proven to perform better compared to previously proposed schemes, providing a valuable contribution to reliable Massive MIMO wireless communication.

关键词： Channel estimation Contamination Interference Maximum likelihood estimation Resource management Spectral efficiency Accuracy Signal to noise ratio Massive MIMO Encoding

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Modeling Framework for Dynamic Pricing in Macroscopic Urban Road Networks

Modeling Framework for Dynamic Pricing in Macroscopic Urban ...

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International Conference on Intelligent Transportation

作者： G.D. Katsifaraki C. Menelaou S. Timotheou C.G. Panayiotou Department of Electrical and Computer Engineering KIOS Research and Innovation Center of Excellence University of Cyprus

ISBN: (数字)9798331505929

ISBN: (纸本)9798331505936

We present a conceptual framework for applying pricing in macroscopic traffic networks, focusing on how traffic flows route themselves when path prices are utilized to manage demand at a macroscopic level. We define the minimum disutility mechanism that guides flows in macroscopic traffic networks, and illustrate the guiding process of flows under the effect of pricing, utilizing simple road network structures. The suggested mechanism forms the cornerstone of the conceptual framework for dynamic pricing in a macroscopic setting, as described herein. This framework highlights that, within a priced network, implementing a dynamic pricing policy to adjust path prices and equalize their disutilities can effectively distribute demand across both spatial and temporal dimensions. To ensure an efficient demand distribution, and thereby keep the network at the non-congested regime, an adequate availability of spatiotemporal paths is required however, both with and without perfect routing information.

关键词： Systematics Roads Focusing Pricing Routing Spatiotemporal phenomena Intelligent transportation systems

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