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3rd International Conference on Advancement in Electronics and Communication Engineering, AECE 2023

作者： Ghassan Majeed, Maryam Qasim, Haitham Bashar Kosimova, Shakhlokhon Diame, Hussein Alaa Lafta, Alaa M. Alchilibi, Haider Al-Kunooze University College Computer Engineer Department Basra Iraq Al-Esraa University College Forensic Sciences Department Baghdad Iraq Fergana Polytechnic Institute Department of Architecture Fergana Uzbekistan National University of Science and Technology Dhi Qar Iraq Al-Farahidi University Medical Laboratory Techniques Department Baghdad10022 Iraq

ISBN: (纸本)9798350330724

The purpose of this paper is to examine and contrast the Novel Circular Patch Antenna employing FR4 and RT Droid (CPA). Resonant frequency of 2.5 GHz in a static medium. To create the antenna, we use the HFSS and two different sample sets. Ten samples are taken from each antenna at 80% G-Power. Assume that the members of Group 1 are all CPAs that use the FR4 substrate. The Novel circular patch antenna (CPA) on RT roger's substrate is in the second group under consideration. This antenna operates at 2.5 GHz to measure reflectance at both ends. A VSWR of 2.1786 dB was measured at 2.4 GHz for a circular CPA designed for RT rogers, while the measured value for a CPA with FR4 was 0.7088 dB. The significance level in SPSS was 0.038 (p 0.05). The findings of this study show that RT rogers is approximately as effective as the FR4 substrate. © 2023 IEEE.

关键词： Natural frequencies

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Coordination of Coupled Electrified Road Systems and Active Power Distribution Networks with Flexibility Integration

SSRN

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SSRN 2024年

作者： Najafi, Arsalan Tsaousoglou, Georgios Gao, Kun Department of Architecture and Civil Engineering Chalmers University of Technology Goteburg SE-412 96 Sweden Department of Applied Mathematics and Computer Science Technical University of Denmark Denmark

Electric Road Systems (ERS) offer a promising solution for mobile charging, reducing the need for mandatory stops to recharge electric vehicles. However, the operational efficiency of ERS is constrained by the limitations of the Power Distribution Network (PDN) responsible for providing electricity. This study proposes an integrated optimization approach for a coupled ERS-PDN system, encompassing traffic assignment and power flow modeling. The optimization considers the presence of self-interested electric vehicle drivers, diverse flexibility resources, and uncertainties in energy supplies, including those arising from renewable sources. To ensure the security of the PDN while supporting ERS, active and flexible energy storage, along with flexible power loads, is employed. The traffic assignment is modeled using a semi-dynamic approach. The proposed optimization framework utilizes a stochastic bi-level optimization based on the Stackelberg game under uncertainty. This approach addresses the joint optimization problem, aiming to minimize the overall cost of the coupled ERS-PDN system and maximize the profit of the energy flexibility provider. The application of Karush Kuhn Tucker conditions transforms the bi-level model into an equivalent single-level model. The results obtained through numerical experiments highlight the effectiveness and benefits of the proposed framework. Specifically, the optimization strategy demonstrates a 3.66% reduction in the burden of ERS on the underlying PDN, emphasizing the positive impact of considering diverse sources of flexibility in the overall system. © 2024, The Authors. All rights reserved.

关键词： Electric power distribution

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URL Spam Detection Using Machine Learning Classifiers

URL Spam Detection Using Machine Learning Classifiers

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Omar Almomani Adeeb Alsaaidah Mosleh M. Abualhaj Mohammed Amin Almaiah Ammar Almomani Shahzad Memon Department of Networks and Cybersecurity Al-Ahliyya Amman University Amman Jordan Department of Computer Science King Abdullah the II IT School The University of Jordan Amman Jordan Department of Information Technology Al-Huson University College Al-Balqa Applied University Irbid Jordan Department of Computer Information Science Higher Colleges of Technology Sharjah United Arab Emirates Department of Computer Science and Digital Technologies Faculty of Architecture Computing and Engineering University of East London UK

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Cybersecurity has emerged as one of the most prevalent and significant challenges in recent years due to the advancement of technology. Among the most frequent and hazardous cybersecurity threats are spam URLs (Uniform Resource Locators), which are also one of the most popular methods for user fraud. Users are the victims of this attack, which also steals their data and infects their devices with harmful software. The detection of spam URLs has become very important in protecting the user. Therefore, this study aims to investigate the efficiency of machine learning classifiers in detecting spam URLs. The following machine learning classifiers were chosen: Random Forest, Decision Tree, and SVM. The evaluation was based on the ISCXURL2016 dataset, which is divided into three groups: All Features, BestFirst Features, and Infogain Features and evaluation matrices were the Accuracy, Precision, Sensitivity, and F-measure. The results obtained showed that Random Forest with All Features is superior to others with an accuracy of 99.75%, Precision of 99.74%, and Sensitivity of 99. 79%, and F-measure 99.76 %.

关键词： Uniform resource locators Support vector machines Sensitivity Accuracy Phishing Metaheuristics Feature extraction Malware Decision trees Random forests

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Integrating Structural Information: Comparing Classification-Based and Segmentation-Based Predictors in Bioinformatics (Case Study: Protein-Peptide Region Residue-Level Interaction) Using Structural Information

Integrating Structural Information: Comparing Classification...

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International CSI computer Conference, CSICC

作者： Shima Shafiee Abdolhossein Fathi Faezeh Safari Department of Computer Engineering and Information Technology Razi University Kermanshah Iran Faculty of Eng. Architecture and Info. Tech. School of Electrical Eng. and Computer Science University of Queensland Brisbane QLD Australia

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

Understanding the regions where proteins bind to peptides is vital for studying diseases like cancer, as it sheds light on various cellular processes and aids in drug discovery. Although researchers can experimentally investigate protein-peptide interactions at the region level by analyzing the structures of their complexes, only a limited number of these structures are known. Additionally, experiments to determine these interactions can be both costly and time-consuming. Computational methods provide an alternative and complementary approach to experimental techniques. As a result, using computational methods to predict peptide-binding regions can significantly enhance the efficiency and cost-effectiveness of experimental research. To leverage these advantages, this study introduces a new method named Structure-based Prediction of Residue-level Interaction of Peptides (SPRINT-Pep), designed to pinpoint the binding regions between proteins and peptides. This approach leverages two distinct predictive models: a classification-based model combining DeepResNet with a Support Vector Machine (SVM) and a segmentation-based model called DeepU-Net. Our predictions outperform other state-of-the-art methods in terms of F-measures (by at least 23%), precision (by at least 3.7%), and the balance between sensitivity and specificity (by about 7.7%). These findings demonstrate the proficiency of the proposed method.

关键词： Proteins Support vector machines Deep learning Peptides Predictive models Sensitivity and specificity Prediction algorithms Robustness Drug discovery Testing

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A Deep Neural Network for Detecting Broken and Scattered Power Lines in Complex Aerial Images 3

A Deep Neural Network for Detecting Broken and Scattered Pow...

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3rd International Conference on Artificial Intelligence and computer Information technology, AICIT 2024

作者： Pei, Qiugen Lai, Yuxuan Wei, Nianxing Wu, Shiting Guangdong Power Grid Co. Ltd. Enterprise Architecture and Digitalization Department Guangzhou China Guangdong University of Technology Dept. School of Computer Science Guangzhou China Guangdong Power Grid Co. Ltd. Huizhou Boluo Power Supply Bureau Huizhou China

ISBN: (纸本)9798350365443

Object detection algorithms constitute a pivotal task in the realm of computer vision, aiming to precisely identify and locate objects within images across diverse scenarios. In power systems, especially in aerial images used to detect wire breaks or scattered anomalies, object detection faces significant challenges. The distribution of wires is often uneven, appearing in different sizes in high-resolution images, and the diversity of fault shapes and sizes makes it difficult for traditional object detection models to recognize accurately. Moreover, the detection of small targets also limits the precision of traditional methods. To address the issue of diversity in fault shapes and sizes in high-resolution images, which makes it difficult for traditional object detection models to recognize accurately, first, we collect fault images of wire breaks and scatter under common scenarios, constructing the 'EWF _detection' (Electric wire fault) dataset, and propose a Pyramid Multi-scale Object Detection Model, establishing the Hybrid Spatial Attention Pooling module, HSAP, which significantly enhances the multi-scale feature extraction capability. It not only improves the model's ability to focus on key features, but also suppresses unnecessary features, enhancing the expressiveness of the network, and effectively adapting to complex backgrounds, variable lighting conditions, and different line scales and directions for transmission line detection needs. This improvement makes our model perform exceptionally well in detecting rare faults in wires and accurately locate fault positions. These achievements provide reliable technical support for wire anomaly detection in the power industry and indicate a broad prospect of application in future power scenarios. Our method not only improves detection accuracy, but also maintains efficient detection speed, reducing reliance on a large number of fault line images. These advantages make our model an important technical tool for pow

关键词： Aerial photography

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Totipotent Neural Controllers for Modular Soft Robots: Achieving Specialization in Body-Brain Co-Evolution Through Hebbian Learning

SSRN

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SSRN 2024年

作者： Ferigo, Andrea Iacca, Giovanni Medvet, Eric Nadizar, Giorgia Department of Information Engineering and Computer Science University of Trento Italy Department of Engineering and Architecture University of Trieste Italy Department of Mathematics and Geosciences University of Trieste Italy Department of Information and Communication Technology

Multi-cellular organisms typically originate from a single cell, the zygote, that then develops into a multitude of structurally and functionally specialized cells. The potential of generating all the specialized cells that make up an organism is referred to as cellular "totipotency", a concept introduced by the German plant physiologist Haberlandt in the early 1900s. In an attempt to reproduce this mechanism in synthetic organisms, we present a model based on a kind of modular robot called Voxel-based Soft Robots (VSRs), where both the body, i.e., the arrangement of voxels, and the brain, i.e., the Artificial Neural Network (ANN) controlling each module, are subject to an evolutionary process aimed at optimizing the locomotion capabilities of the robot. In an analogy between totipotent cells and totipotent ANN-controlled modules, we then include in our model an additional level of adaptation provided by Hebbian learning, which allows the ANNs to adapt their weights during the execution of the locomotion task. Our in silico experiments reveal two main findings. Firstly, we confirm the common intuition that Hebbian plasticity effectively allows better performance and adaptation. Secondly and more importantly, we verify for the first time that the performance improvements yielded by plasticity are in essence due to a form of specialization at the level of single modules (and their associated ANNs): thanks to plasticity, modules specialize to react in different ways to the same set of stimuli, i.e., they become functionally and behaviorally different even though their ANNs are initialized in the same way. This mechanism, which can be seen as a form of totipotency at the level of ANNs, can have, in our view, profound implications in various areas of AI and applications thereof, such as modular robotics and multi-agent systems. © 2024, The Authors. All rights reserved.

关键词： Neural networks

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Towards Deep Application-Network Integration: architectures, Progress and Opportunities

arXiv

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arXiv 2024年

作者： Serracanta, Berta Gao, Kai Ros-Giralt, Jordi Rodriguez-Natal, Alberto Contreras, Luis M. Yang, Richard Cabellos, Albert UPC BarcelonaTech Spain The Department of Computer Science and Techonology Tsinghua University China Qualcomm Europe Inc. The Enterprise Networking CTO team at Cisco Technology Expert at Telefonica CTIO Computer Science and Electrical Engineeringsince The Computer Architecture Department Universitat Politècnica de Catalunya Spain

With the rise of a new generation of applications (e.g., virtual and augmented reality, artificial intelligence, etc) demanding stringent performance requirements, the need for networking solutions and architectures that can enable a higher Quality of Experience (QoE) is becoming increasingly important. While jointly optimizing application and network may increase the applications' QoE and simultaneously improve the utilization of network resources, such a paradigm has had limited success in real production networks. However, with the combination of revolutionary trends in (1) compute processing demands, (2) networking capabilities, and (3) sustainable business models, it is high time the community explores the full potential of deeper integration between application and network. In this paper, recent trends observed over the past few years are systematically reviewed. These include the paradigm shift in modern communication services towards computing-driven applications, such as on-site AI training, advances in programmable network technologies like Software Defined Networking (SDN), and new business models incentivizing collaboration and cooperation between parties. Following this, successful scenarios that benefit from various forms of deeper network-application integration are reported, highlighting their considerable potential. A unified framework is then introduced, providing an overview of possible architecture paradigms for network-application integration and bringing awareness to existing abstractions, mechanisms, tools, and their potential combinations. The paper concludes with a discussion of several remaining challenges in building practical network-application integrated systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Quality of service

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Module-Based Approach for Detecting Performance Bugs in Java Applications at Scale

Module-Based Approach for Detecting Performance Bugs in Java...

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Annual Conference of Industrial Electronics Society

作者： Yingying Wen Guanjie Cheng Department of Architecture and Technique Innovation Huawei Cloud Hangzhou China Department of Computer Science and Technology Zhejiang University Hangzhou China

Hundreds of thousands of Java applications have been deployed in data centers at our production Cloud to support burst peak traffic. However, detecting performance bugs in Java can be difficult as they may not prevent the applications from running correctly, and may not even manifest at low loads. Profiling data collected from production provides insight into the actual running states of applications. In this paper, we aim to identify hot spots for further performance debugging by analyzing profiling data from tens of thousands of machines in the data center using a module-based approach. We present our practical experience with module classification, which allows for filtering of out-of-range modules and long-duration modules of high utilization. Our study proposes a heuristic solution to detect performance bugs in Java at scale.

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Impact of Virtual Synchronous Generators on Power System Frequency Dynamics

Impact of Virtual Synchronous Generators on Power System Fre...

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International Conference on Smart Energy Systems and Technologies (SEST)

作者： Aihui Fu Aleksandra Lekić Eleftherios O. Kontis Kyriaki-Nefeli D. Malamaki Georgios C. Kryonidis Juan Manuel Mauricio Charis S. Demoulias Miloš Cvetković Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft The Netherlands Department of Electrical & Computer Engineering Aristotle University of Thessaloniki Thessaloniki Greece Department of Electrical Engineering University of Seville Spain

This paper deals with a systematic assessment of the power system frequency dynamics under high penetration of converter-interfaced renewable energy sources (CI-RESs). Specifically, the concept of the virtual synchronous generator (VSG) is implemented in the CI-RESs located at the transmission system (TS) side and/or the distribution network (DN) side. Dynamic RMS simulations are performed on a testbed consisting of the IEEE 9-bus TS grid and the CIGRE medium-voltage DN grid under different CI-RES penetration levels and VSG control parameters to assess the VSG impact on the power system frequency dynamics. It is shown that the decommissioning of conventional power plants coupled via synchronous generators can be safely performed in case the VSG concept is adopted correctly.

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IoT Based Systems and Services: Recent Security Concerns and Feasible Solutions

IoT Based Systems and Services: Recent Security Concerns and...

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Humanitarian technology Conference (KHI-HTC), IEEE Karachi Section

作者： Hafiz Muhammad Attaullah Shahzad Memon Ozlem Feyza Erkan Ruba Khawar Department of Computer Science Mohammad Ali Jinnah University Karachi Pakistan Department of Computer Engineering Faculty of Engineering and Architecture Beykoz University Istanbul Türkiye Faculty of Information Sciences and Humanities NED University of Engineering and Technology Karachi Pakistan

The term Internet of Things (IoT) is used to refer as embedded devices or objects with internet access, allowing them to communicate globally, interacting with people and networks. IoT security issues are directly related to the widespread use of its systems. This paper examines the security technologies employed in IoT and their respective features and applications. Additionally, it addresses the various security concerns associated with IoT-based systems and services, highlighting their occurrence within the three-layer framework of IoT. These days, as IoT introduces advanced application architectures that minimizes human intervention by integrating businesses in some way to address digital computer processes by communicating within a given network. Moreover, it will be discussed the key technologies associated with Advanced Encryption Standards (AES) and the changes brought in market value and usage of services that has been taken place b since the IoT incorporated.

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