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IEEE International Conference on Smart Instrumentation, Measurement and Applications (ICSIMA)

作者： Muhammad Iqbal Yusri Mohamed Hadi Habaebi Teddy Surya Gunawan Hasmah Mansor Mira Kartiwi Levy Olivia Nur Electrical and Computer Engineering Dept. International Islamic University Malaysia Kuala Lumpur Malaysia Information Systems Department International Islamic University Malaysia Kuala Lumpur Malaysia School of Electrical Engineering Telkom University Bandung Indonesia

ISBN: (数字)9798350365283

ISBN: (纸本)9798350365290

Intrusion Detection systems (IDS) are indispensable for cybersecurity, as they safeguard networks from increasingly sophisticated and sophisticated cyberattacks. This paper assesses the influence of dataset balancing on the performance of machine learning-based IDS, thereby addressing the challenge of imbalanced data in detecting network intrusions. We concentrate on three IDS implementations: Tree-based Intelligent IDS, Multi-Tiered Hybrid IDS (MTH-IDS), and Leader Class and Confidence Decision Ensemble (LCCDE). We utilized the Synthetic Minority Over-Sampling Technique (SMOTE) to balance data and implemented feature selection and hyperparameter optimization to improve the model's performance using the CICIDS 2017 dataset. The LCCDE model exhibits the highest performance, as our comparative analysis demonstrates that the combination of SMOTE and feature selection enhances the F1 scores. The results underscore the significance of advanced ensemble techniques and data preprocessing in developing resilient IDS. This research emphasizes the necessity of ongoing optimization and evaluation of IDS models to guarantee effective protection against the development of cyber threats.

关键词： Heuristic algorithms System performance Roads Intrusion detection Feature extraction Real-time systems Delays

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Voltage Profile Improvement in Unbalanced Distribution Networks for Probabilistic Generation and Consumption

Voltage Profile Improvement in Unbalanced Distribution Netwo...

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International Conference on Probabilistic Methods Applied to Power systems (PMAPS)

作者： Mohammed Bamatraf Oğuzhan Ceylan Ioana Pisica Aydoğan Özdemir Department of Electrical Eng. Istanbul Technical University Turkey Department of Management Information Systems Kadir Has University Turkey Department of Electrical and Computer Engineering Brunel University London UK Department of Electrical and Electronics Engineering Kadir Has University Turkey

ISBN: (数字)9798350372786

ISBN: (纸本)9798350372793

Due to their technical, economical, and environmental advantages, active distribution networks implement renewable energy resources (RERs) such as photovoltaic (PV) units in distribution networks DNs. However, some drawbacks may arise due to the intermittent nature of RERs, such as voltage fluctuations and increased system losses. This paper presents an optimization problem that is solved by sequential linear programming (SLP) to improve the voltage profile of the unbalanced distribution network. A probabilistic approach was applied to both the load profile and the active power generation of the PV units. SLP is applied to the modified IEEE 34 Bus Test system. The method optimizes the voltage deviations by changing the taps of the voltage regulators and the reactive power injected by the inverters of the PV systems and, in some cases, by switching a shunt capacitor. MATLAB simulations are done at different times of the day with different loads and PV outputs to compare base case and optimal case voltage profiles. The results show better voltage profiles after applying the presented approach.

关键词： Reactive power Regulators Voltage fluctuations Capacitors Distribution networks Switches Probabilistic logic

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3D Printed Wideband Multilayered Dual-Polarized Stacked Patch Antenna with Integrated MMIC Switch

IEEE Open Journal of Antennas and Propagation

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IEEE Open Journal of Antennas and Propagation 2021年 2卷 38-48页

作者： Kacar, Merve Weller, Thomas M. Mumcu, Gokhan Center for Wireless and Microwave Information Systems University of South Florida TampaFL United States School of Electrical Engineering and Computer Science Oregon State University CorvallisOR United States

Capability of 3D printing for developing wideband multilayered antenna systems packaged with active RF circuit components remains relatively unexplored. To address this gap, this manuscript demonstrates wideband patch antenna that is integrated with a polarization selection switch. Antenna bandwidth is enhanced with customized substrate thicknesses and stacked patch layers. Switch is integrated with 3D vertical transitions to achieve wide bandwidth, smaller packaging, and low-loss. To enable future antenna array applications, a detailed investigation is also presented to maximize the performance of the microstrip feed lines by adjusting the substrate thickness and deposition process. The antenna consists of 13 dielectric and conductive layers. Specifically, the manuscript demonstrates the lowest attenuation in the literature for a fully printed microstrip line with 0.25 dB/cm measured loss at 18 GHz. The antenna operates with more than 80% radiation efficiency and 45% bandwidth. The polarization switch is embedded within the antenna structure. The antenna retains a high cross-polarization ratio of 20 dB due to 3D feed line transitions and symmetric location of the switch with respect to the antenna feeds. © 2020 IEEE.

关键词： Microstrip antennas

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A Study of Two Types of Introductory Exercise Problems for Network Programming Learning with Python 23

A Study of Two Types of Introductory Exercise Problems for N...

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11th International Conference on computer and Communications Management, ICCCM 2023

作者： Lu, Xiqin Funabiki, Nobuo Ueda, Kiyoshi Kao, Wen-Chung Department of Information and Communication Systems Distributed System Design Lab. Okayama University Okayama Japan Department of Computer Science Control Software Lab. Nihon University Koriyama Japan Department of Electrical Engineering National Taiwan Normal University Taipei Taiwan

ISBN: (纸本)9798400707735

Network programming is the act of writing computer codes for communications between programs or processes in different computers across networks. It is crucial for applications and services using networks, including e-commerce, gaming, and social media. However, university courses of educating it are very limited. As a result, it has become important to develop and offer IT tools to support self-studies of network programming by novices. In this paper, we present a study of two types of introductory exercise problems, namely, Grammar-concept Understanding Problem (GUP) and Comment Insertion Problem (CIP), for self-studies of five basic network protocols of TCP/IP, ARP, SMTP, POP3, and SSH with Python. A GUP instance requests to answer the keyword such as a library function in the given source code that corresponds to the grammar concept described in the question. A CIP instance requests to insert the comment at each blank of the code. String matching is adopted to mark any answer at the browser-based answer interface. Through solving them, it is expected to understand the meaning of important words and the code structure for network programming. As preliminary evaluations, we generated six GUP and CIP instances respectively, and assigned them to 10 students in Okayama University, Japan. The results confirmed that the proposal is proper for novices to learn network programming with Python. © 2023 ACM.

关键词： Python

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Proposed Flight Recorder Recommendations for Unmanned Aircraft systems Integration into the National Airspace System

Proposed Flight Recorder Recommendations for Unmanned Aircra...

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Conference on Digital Avionics systems (DASC)

作者： Olayemi Ogirimah Waleed Raza Gurvir Bawa Justus Renkhoff Houbing Song Richard S. Stansbury Electrical Engineering and Computer Science Embry-Riddle Aeronautical University Daytona Beach USA Department of Information Systems University of Maryland - Baltimore County Baltimore USA

ISBN: (数字)9798350349610

ISBN: (纸本)9798350349627

The increasing integration of Unmanned Aircraft systems (UAS) into the National Airspace System necessitates the development of flight data recorder (FDR) recommendations tailored specifically for these systems. Traditional FDRs used in manned aviation are inadequate for capturing the flight data required for UAS operations. This study involved a detailed review of existing guidelines and regulations for manned aircraft FDRs, adapting and extending them to meet the unique needs of UAS operations. By drawing upon industry best practices and aca-demic research, a holistic framework for UAS FDRrecommendations is proposed. This framework out-lines the essential data parameters and recommended recording intervals required to facilitate detailed in-cident/accident investigations and continuous safety enhancements. Implementing UAS-specific FDR stan-dards is crucial for realizing the full potential of unmanned aviation while upholding stringent safety and operational standards.

关键词： Industries Regulators Reviews System integration Aerospace electronics Regulation Safety Aircraft Standards Guidelines

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Repairing Learning-Enabled Controllers While Preserving What Works

arXiv

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arXiv 2023年

作者： Lu, Pengyuan Cleaveland, Matthew Sokolsky, Oleg Lee, Insup Ruchkin, Ivan Computer and Information Science University of Pennsylvania PhiladelphiaPA United States Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA United States Electrical and Computer Engineering University of Florida GainesvilleFL United States

Learning-enabled controllers have been adopted in various cyber-physical systems (CPS). When a learning-enabled controller fails to accomplish its task from a set of initial states, researchers leverage repair algorithms to fine-tune the controller’s parameters. However, existing repair techniques do not preserve previously correct behaviors. Specifically, when modifying the parameters to repair trajectories from a subset of initial states, another subset may be compromised. Therefore, the repair may break previously correct scenarios, introducing new risks that may not be accounted for. Due to this issue, repairing the entire initial state space may be hard or even infeasible. As a response, we formulate the Repair with Preservation (RwP) problem, which calls for preserving the already-correct scenarios during repair. To tackle this problem, we design the Incremental Simulated Annealing Repair (ISAR) algorithm, which leverages simulated annealing on a barriered energy function to safeguard the already-correct initial states while repairing as many additional ones as possible. Moreover, formal verification is utilized to guarantee the repair results. Case studies on an Unmanned Underwater Vehicle (UUV) and OpenAI Gym Mountain Car (MC) show that ISAR not only preserves correct behaviors from previously verified initial state regions, but also repairs 81.4% and 23.5% of broken state spaces in the two benchmarks. Moreover, the average signal temporal logic (STL) robustnesses of the ISAR repaired controllers are larger than those of the controllers repaired using baseline methods. © 2023, CC BY.

关键词： Simulated annealing

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Spiking Neural Networks for sEMG-Based Hand Gesture Recognition

Spiking Neural Networks for sEMG-Based Hand Gesture Recognit...

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IEEE International Conference on systems, Man and Cybernetics

作者： Mansooreh Montazerin Farnoosh Naderkhani Arash Mohammadi Department of Electrical and Computer Engineering Concordia University Montreal QC Canada Concordia Institute for Information Systems Engineering (CIISE) Concordia University Montreal QC Canada

Given the recent surge of significant interest in implementing intelligent hand gesture recognition methods in human-machine interface systems, a wide variety of Deep Neural Networks (DNNs) have been proposed in the literature. In this paper, we introduce a novel and compact Spiking Neural Network (SNN) model for hand gesture recognition using High-Density surface Electromyogram (HD-sEMG) signals. Capitalizing on their ability to extract spatiotemporal features of HD-sEMG signals along with their proven strength in imitating human brain's neural activity using event-driven data processing, we used SNNs as the main building block of our proposed hand gesture recognition model. We show that our proposed model can efficiently differentiate 14 hand movements by considering each sample of the HD-sEMG data as a single time step for the SNN architecture. Moreover, we show that the proposed SNN model does not require huge pre-processing, spike encoding and feature extraction tasks and works effectively on Min-Max normalized continuous-value sEMG signals. We evaluate our SNN model using a 5-fold cross-validation scheme and categorize different participants based on the range of classification accuracy we obtained for them. The following results are acquired by segmenting HD-sEMG signals into windows of size 62.5ms with no overlap. The proposed method led to 6 out of 19 subjects achieving average classification accuracy of ≥ 80% with maximum accuracy of 98% associated with 3 rd session of the sEMG dataset as the test set.

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CosSIF: Cosine similarity-based image filtering to overcome low inter-class variation in synthetic medical image datasets

arXiv

引用

arXiv 2023年

作者： Islam, Mominul Zunair, Hasib Mohammed, Nabeel Department of Electrical and Computer Engineering North South University Bashundhara Dhaka Bangladesh Concordia Institute for Information Systems Engineering Concordia University MontrealQC Canada

Crafting effective deep learning models for medical image analysis is a complex task, particularly in cases where the medical image dataset lacks significant inter-class variation. This challenge is further aggravated when employing such datasets to generate synthetic images using generative adversarial networks (GANs), as the output of GANs heavily relies on the input data. In this research, we propose a novel filtering algorithm called Cosine Similarity-based Image Filtering (CosSIF). We leverage CosSIF to develop two distinct filtering methods: Filtering Before GAN Training (FBGT) and Filtering After GAN Training (FAGT). FBGT involves the removal of real images that exhibit similarities to images of other classes before utilizing them as the training dataset for a GAN. On the other hand, FAGT focuses on eliminating synthetic images with less discriminative features compared to real images used for training the GAN. Experimental results reveal that employing either the FAGT or FBGT method with modern transformer and convolutional-based networks leads to substantial performance gains in various evaluation metrics. FAGT implementation on the ISIC-2016 dataset surpasses the baseline method in terms of sensitivity by 1.59% and AUC by 1.88%. Furthermore, for the HAM10000 dataset, applying FABT outperforms the baseline approach in terms of recall by 13.75%, and with the sole implementation of FAGT, achieves a maximum accuracy of 94.44%. Code and implementation details are available at: https://***/mominul-ssv/cossif. © 2023, CC BY.

关键词： Generative adversarial networks

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Periodic-Enhanced Informer Model for Short-Term Wind Power Forecasting Using SCADA Data

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IEEE Transactions on Sustainable Energy 2025年

作者： Liu, Zhao-Hua Li, Long-Wei Wei, Hua-Liang Li, Ming Lv, Ming-Yang Zhang, Ying-Jie Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China University of Sheffield Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Supervisory Control and Data Acquisition (SCADA) systems can collect abundant information about wind farm operation and environment. To better make use of SCADA data, a periodic-enhanced informer model for short-term wind power forecasting using scada data is proposed. Firstly, to effectively filter out noise in SCADA data, a v-p curve-based method is adopted by incorporating a quartile approach to remove sparse outliers;a density-based spatial clustering of applications with noise (DBSCAN) algorithm is then employed to eliminate stacked outliers from the power curve. Secondly, a multi-feature input set selection method based on Maximization information Coefficient is introduced to make better use of the SCADA system data by reducing the number of features. Thirdly, a Temporal Convolutional Network (TCN) is designed to extract the scalar projection of the input set, followed by fusing the local time stamp and global time stamp to build the periodic information enhanced prediction model embedding layer. Subsequently, the enhanced input set is fed into an informer model to predict future wind power. Finally, considering the multiple temporal scales structure characteristics present in the dataset, a multi-scale deep fusion module is established in the informer model to deeply integrate the features of different scales. It can simultaneously avoid the resource waste and overfitting problems caused by increasing the network depth. The experimental results, which are obtained from several deep learning methods on real SCADA data, demonstrate that the suggested approach has good predictive capability. © 2010-2012 IEEE.

关键词： Wind forecasting

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Indoor Deterministic-Based Channel Modeling at D-Band for 6G Wireless Networks

Indoor Deterministic-Based Channel Modeling at D-Band for 6G...

引用

IEEE Conference on Vehicular Technology (VTC)

作者： Nektarios Moraitis Demosthenes Vouyioukas School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Dept. of Information and Communication Systems Engineering University of the Aegean Samos Greece

This paper presents the preliminary results of a propagation study at D-band based on deterministic modeling for an indoor line-of-sight (LOS) environment. The proposed channel model is based on multiple rays between a fixed transmitter and a moving receiver. The results reveal that the antenna characteristics significantly affect the propagation along the corridor, which is also reflected to the produced power delay profiles of the channel. Furthermore, the existence of side-corridors also influences the channel parameters, reducing the root-mean-square (rms) delay and angular spread values. At 12-m distance away from the transmitter the rms delay spread varies between 0.1 and 4.3 ns, whereas the azimuth and elevation rms angular spreads (AS) are in the range of 0.7°-12.4° and 2.1°-7.0°, respectively, depending on the selected scenario and antenna configuration.

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