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5th IEEE International Conference on Modern Electrical and Energy System, MEES 2023

作者： Kovac, Dobroslav Melnykov, Viacheslav Technical University of Kosice Faculty of Electrical Engineering and Informatics Dept. of Theoretical and Industrial Electrical Engineering Kosice Slovakia Institute of Electromechanics Energy Saving and Control Systems Kremenchuk Mykhailo Ostrohradskyi National University Kremenchuk Ukraine

ISBN: (纸本)9798350359787

The paper deals with a new algorithm for controlling a multi-phase buck DC-DC converter. The principle of its control is explained in detail using a flowchart. It is based on the continuous change of the converter topology using the number of its currently controlled phases depending on the size of the input power. In connection with this, its microprocessor control is also mentioned. The whole issue is explained on the design, simulation and measurement of a laboratory sample of a four-phase buck converter. © 2023 IEEE.

关键词： Solar panels

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Scalability in Autoencoder-based OFDM Communication System 2

Scalability in Autoencoder-based OFDM Communication System

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2nd IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2023

作者： Tsugawa, Seizan Toma, Takao Oshiro, Shiho Wada, Tomohisa University of the Ryukyus Computer Science and Intelligent Systems Program Okinawa Japan Magna Design Net Inc Okinawa Japan University of the Ryukyus Information Technology Center Okinawa Japan University of the Ryukyus Area of Computer Science and Intelligent Systems Dept. of Engineering Okinawa Japan

ISBN: (纸本)9798350305142

This paper proposed Scalability in Autoencoder-based Orthogonal Frequency Division Multiplexing(OFDM) communication system. In the previous research, only the comparison between IEEE802.11a and Autoencoder by the conventional OFDM communication system was performed, and it was proved that the communication system created by Autoencoder exceeded the performance of the conventional system. Therefore, in this paper, it uses IEEE802.11n and compare whether it can be improved by expanding the bandwidth and using ***802.11n standard has an FFT length of 128, a subcarrier number of 114 (108 for data), and modulation schemes of Quadrature Phase Shift Keying(QPSK), 16 Quadrature Amplitude Modulation (16QAM). The GI length is 800ns and the symbol length is 4000ns. In the simulation, a computer simulation was performed using a conventional OFDM communication system and a communication system generated by Autoencoder. Assuming that the simulation environment had an Signal-to-Noise Ratio (SNR) of 0 to 30 and an amplitude r of 0.0 to 1.0, the Symbol Error Rate (SER) status for each SNR was output. As a result of computer simulation, QPSK converged at SNR 27 at IEEE 802.11a, but was able to reduce SER overwhelmingly to SNR 12 at IEEE 802.11n. Also, in 16QAM the convergence at r=0.0 is the same as for SNR22 , but in IEEE 802.11a it does not converge after r=0.6, but in IEEE 802.11n it does not converge only at r=0.9 and r=1.0. As a future task, it will use IEEE802.11ac, which enables communication speeds several times faster than IEEE802.11n, examine whether it is possible to further improve accuracy. And it will continue our research to correspond MIMO communication. © 2023 IEEE.

关键词： Orthogonal frequency division multiplexing

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Bidirectional Multi-Step Domain Generalization for Visible-Infrared Person Re-Identification

arXiv

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arXiv 2024年

作者： Alehdaghi, Mahdi Shamsolmoali, Pourya Cruz, Rafael M.O. Granger, Eric LIVIA ILLS Dept. of Systems Engineering ETS Montreal Canada Dept. of Computer Science University of York United Kingdom

A key challenge in visible-infrared person re-identification (V-I ReID) is training a backbone model capable of effectively addressing the significant discrepancies across modalities. State-of-the-art methods that generate a single intermediate bridging domain are often less effective, as this generated domain may not adequately capture sufficient common discriminant information. This paper introduces Bidirectional Multi-step Domain Generalization (BMDG), a novel approach for unifying feature representations across diverse modalities. BMDG creates multiple virtual intermediate domains by learning and aligning body part features extracted from both I and V modalities. In particular, our method aims to minimize the cross-modal gap in two steps. First, BMDG aligns modalities in the feature space by learning shared and modality-invariant body part prototypes from V and I images. Then, it generalizes the feature representation by applying bidirectional multi-step learning, which progressively refines feature representations in each step and incorporates more prototypes from both modalities. Based on these prototypes, multiple bridging steps enhance the feature representation. Experiments1 conducted on V-I ReID datasets indicate that our BMDG approach can outperform state-of-the-art part-based and intermediate generation methods, and can be integrated into other part-based methods to enhance their V-I ReID performance. © 2024, CC BY.

关键词： Feature extraction

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Explainable Long-Term Forecasting of Air Pollution

Explainable Long-Term Forecasting of Air Pollution

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IEEE International Conference on Big Data

作者： Abdul Mutakabbir Samuel A. Ajila Dept. of Systems and Computer Engineering Carleton University Ottawa ON Canada

Long-term air pollution forecasting is essential for making public policies and issuing warnings. This will reduce the impact of pollution on the environment and human health. This research focuses on achieving long-term forecasting of air pollution attributes $(PM 2.5, PM_{10}, SO_{2}$, $NO_{2}, CO$, and $O_{3})$ by providing minimal historical data as input to the model. The best-performing models in this research produced between a 1% increase in RMSE for certain pollutants to a 50% decrease in RMSE for other air pollution attributes (except $CO)$ compared to the initial research on compositional learning, while being able to forecast the trend and seasonality accurately for more than one year into the future. The training time for each pollution attribute model dropped to 4 seconds compared to 1800 seconds in the case of the compositional learning method. This paper also delves into the challenges with long-term forecasting for the Beijing air quality dataset and discusses approaches to overcome these challenges.

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Intelligent Constellation Generation based on Autoencoder Communication System 2

Intelligent Constellation Generation based on Autoencoder Co...

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2nd IEEE International Conference on computer Vision and Machine Intelligence, CVMI 2023

作者： Matsumoto, Kaisei Toma, Takao Oshiro, Shiho Wada, Tomohisa Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Area of Computer Science and Intelligent Systems University of the Ryukyus Dept. of Engineering Okinawa Japan

ISBN: (纸本)9798350305142

This paper discusses intelligent constellation generation based on autoencoder communication system. In previous studies, the amplitude was set to fluctuate between r=0.0 and 1.0. However, when checking the generated constellation, distortion was confirmed instead of the conventional symbol arrangement. Therefore, in this paper, it compares the case where the amplitude is constant, the case where the average amplitude within a Minibatch is 1, and the case where the average amplitude is 1 for Interval time. The communication standard used in this research is IEEE 802.11a, assuming wireless Local Area Network (LAN) specifications. The IEEE 802.11a standard has an Fast Fourier Transform (FFT) length of 64, a subcarrier number of 52, and Quadrature Phase Shift Keying (QPSK) and 16 Quadrature Amplitude Modulation (QAM), modulation methods. A guard interval of 800 ns is added and the symbol length is 4000 ns. First, a simulation was performed under the condition that the amplitude was kept constant. QPSK with 4 symbols, constant amplitude model is rounded more than previous research result. 16QAM with 16 symbols is arranged regularly like lined up on a line. Second, the simulation was performed under the condition that the average amplitude within the minibatch was set to 1. QPSK with 4 symbols, appears to rotate clockwise. 16QAM with 16 symbols has a more uniform symbol placement than previous research result. Third, a simulation was performed under the condition that the average amplitude within Interval time was set to 1. QPSK with 4 symbols, is the closest to square among QPSK output results so far. The direction is slightly tilted, but if it can be rotated a little more, it may be possible to reproduce the same symbol arrangement as before. 16QAM with 16 symbols, the symbol arrangement is biased as a whole. However, it can be seen that are arranged in line on the line, perhaps due to regularity. As future work, in addition to the conditions set this time, it will exa

关键词： Quadrature phase shift keying

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RAN Explainable Anomaly Prediction for 6G Networks 29

RAN Explainable Anomaly Prediction for 6G Networks

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29th IEEE International Workshop on computer Aided Modeling and Design of Communication Links and Networks, CAMAD 2024

作者： Marantis, Panagiotis Ramantas, Kostas Verikoukis, Christos Universitat Politècnica de Catalunya Dept. of Signal Theory and Communications Barcelona Spain Iquadrat Informatica S.L. Barcelona Spain University of Patras Dept. of Computer Engineering and Informatics Patras Greece Athena Research Center Athens Greece Industrial Systems Institute Patras Greece

ISBN: (纸本)9798350377644

Machine Learning (ML) models integrated into future 6G network architectures are anticipated to address the demanding task of Anomaly Detection (AD) at the Radio Access Network (RAN). A challenging but essential aspect of this task is forecasting anomalies ahead of time, aiming to enable automated interventions to proactively prevent or mitigate service degradation. To address this challenge, we propose an Explainable Anomaly Prediction (XAP) framework, that combines a time series forecasting Long Short-Term Memory Encoder- Decoder (LSTM-ED) model, and an Isolation Forest (IF) anomaly detector whose output is interpreted using the SHAP algorithm. The framework is evaluated using a real- world dataset containing RAN Key Performance Indicators (KPIs) collected from a User Equipment (UE) device. The results validate the effectiveness of our framework in predicting RAN anomalies and identifying the specific KPIs linked to each predicted anomaly. © 2024 IEEE.

关键词： Radio access networks

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Designing and Developing a Mobile Application for Monitoring & Visualizing Blood Pressure Data

Designing and Developing a Mobile Application for Monitoring...

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IEEE Sensors Applications Symposium, SAS

作者： Mahsa Sinaei Hamed Laura Reid Alice Olorunnife David Casciano Fateme Rajabiyazdi dept. Systems and Computer Engineering Carleton University Ottawa Canada

High blood pressure is one of the major causes of cardiovascular diseases, renal failure, and even sudden death. To avoid developing these health issues, it is important to consistently monitor blood pressure levels. Monitoring blood pressure levels regularly allows people to observe changes in their blood pressure measurements and contact their healthcare providers for guidance if needed. The aim of this paper is designing and developing a mobile application that can assist individuals to better understand the changes in their blood pressure levels by introducing a novel approach to visualizing blood pressure data and managing missing data. Employing a user-centered design approach, we designed and developed an app. To complete this study, we conducted a literature review to identify key design requirements for such apps and then, designed our low-fidelity prototype based on these design requirements. Next, we consulted with two experts to obtain their feedback on the content, presentation, and usability of our initial low-fidelity prototype. Based on these experts' feedback we designed our mid-fidelity and high-fidelity prototype. Eventually, we conducted a user evaluation study to evaluate our high-fidelity prototype. The results demonstrated this app provides a clear visual representation of blood pressure measurements over time.

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Service Function Chaining Implementation using VNFs and CNFs

Service Function Chaining Implementation using VNFs and CNFs

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IEEE International Conference on Cloud engineering (IC2E)

作者： Abdullah Bittar Ziqiang Wang Changcheng Huang Dept. of Systems and Computer Engineering Carleton University Ottawa Canada

The increasing popularity of cloud-based platforms has led to the emergence of two prominent open-source solutions: OpenStack and Kubernetes. OpenStack facilitates computing and networking resources through virtual machine instances, while Kubernetes excels in container orchestration, managing containerized workloads and services. This paper explores the implementation of Service Function Chaining (SFC) using a combination of virtual machines in OpenStack and containers in Kubernetes. The primary focus of our study is to analyze the performance of containers within the context of chain deployment in Kubernetes. Our experimentation reveals compelling insights into container bootup times, showcasing their efficiency when compared to virtual machines. Additionally, we meticulously evaluate the impact of integrating SFC-related interfaces into pods forming a chain, particularly assessing CPU, memory, and bandwidth utilization. Our findings underline the advantages of utilizing containers in SFC deployment scenarios and shed light on the potential overhead that arises during interface integration.

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control of the organizational System Configuration in the Context of Digital Transformation 6

Control of the organizational System Configuration in the Co...

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6th International Scientific Conference on Information, control, and Communication Technologies, ICCT 2022

作者： Protalinsky, Oleg M. Khanova, Anna A. Bondareva, Irina O. Nesterova, Evgeniya T. Moscow Power Engineering Institute Dept. of Automated Control Systems of Thermal Processes Moscow Russia Astrakhan State Technical University Dept. Applied Informatics Astrakhan Russia

ISBN: (数字)9781665493673

ISBN: (纸本)9781665493673

With the development of digitalization and digital transformation processes, there is a concern of consolidating strategic control methods based on the balanced scorecard (BSC), data envelopment analysis (DEA) and logical-and-probabilistic approach, as well as creating on their basis a conceptual control scheme of organizational systems configurations. The question of forming a strategic map of digital transformation based on artificial neural networks is considered. In order to assess the effectiveness it is proposed to use a bunch of BSC-DEA methods. To assess the risk of failure to achieve the strategic objectives of the digital transformation of organizational systems, a bundle of MTSP-logic-probabilistic model was proposed. It is proposed to use a digital twin of the organizational system based on a simulation model to test the feasibility of control decisions in the context of digital incrementation in various situations of operation of the organizational system, at various stages of the intra-configuration cycle. © 2022 IEEE.

关键词： Data envelopment analysis

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Applying Deep Learning Methods on Spam Review Detection

Applying Deep Learning Methods on Spam Review Detection

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International Conference on Computing Methodologies and Communication (ICCMC)

作者： M. Ramu Chinnakotla Jayanth Raj Apthiri Nithish Chandhu Boggula Ganesh Babu G Srikanth K.I Goud Dept. of computer science and systems engineering SVEC Tirupati andhrapradesh

In today's environment, a reliable and effective technique for identifying spam reviews is essential if you want to purchase things online without being taken advantage of. There are possibilities for publishing reviews in many internet locations, which opens the door for sponsored or deceptive fake reviews. These fabricated evaluations may mislead the general audience and leave them unsure of whether or not to believe them. The issue of spam review finding has been solved by the introduction of prominent deep literacy methods. The focus of recent research has been on supervised literacy practices that contain labelled data, which is inadequate for online review. This initiative aims to expose any dishonest textbook reviews. To do this, we've used both labelled and unlabeled data and suggested deep learning techniques for spam review detection, including Multi-Layer Perceptron (MLP), Convolutional Neural Networks (CNN), and a Long Short-Term Memory (LSTM) variation of Recurrent Neural Networks (RNN). We also used standard machine learning classifiers to identify spam reviews, including Naive Bayes (NB), K Nearest Neighbor (KNN), and Support Vector Machine (SVM). Finally, we compared the effectiveness of traditional and deep literacy classifiers. We'll use deep literacy classifiers to boost the finesse and efficiency.

关键词： Support vector machines Deep learning Recurrent neural networks Publishing Manuals Programming Convolutional neural networks

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