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9th International Conference on Systems and informatics, ICSAI 2023

作者： Davidrajuh, Reggie University of Stavanger Dept. Electrical Engineering and Computer Science Stavanger Norway

ISBN: (纸本)9798350383706

During the development stages of the modeling processes, there is always a need to create random Petri Nets so that these Petri Nets can be used as test cases. This paper presents new functions for the automated generation of random Petri Nets. The new functions are implemented in the General-purpose Petri Net Simulator (GPenSIM), a tool that runs on the MATLAB platform. Some researchers use GPenSIM for modeling, simulation, and performance analysis of discrete systems. Newer applications of GPenSIM also include modeling and control of cyber-physical systems using distributed modules known as 'Petri Modules.' Firstly, this paper introduces GPenSIM and a few Petri Net types, such as event graphs and Petri modules. Then, the newer functions of GPenSIM for creating random Petri Nets are presented, which is the goal of this paper. © 2023 IEEE.

关键词： Petri nets

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CRISP - DM Process in Smart Building Management 11

CRISP - DM Process in Smart Building Management

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11th International Conference on electrical and Electronics engineering, ICEEE 2024

作者： Dursun, Mahir Sabyrzhan, Moldir Gazi University Faculty of Technology Dept. of Electrical and Electronics Engineering Ankara Turkey Graduate School of Informatics Gazi University Department of Computer Science Ankara Turkey

ISBN: (纸本)9798350362541

Smart Buildings are buildings that integrate and combine intelligence, management, control, materials, and construction as a single system to meet all the needs of the building, such as comfort, energy efficiency, and durability. In universities, laboratories are used less frequently than other facilities and classrooms. Courses and students may change in the same classroom, and the period of use of such classrooms is much more extended than in laboratories. However, classrooms may only be used for classes during long breaks or for short periods of non-use between exams. In addition, there is no provision for when the heating system will start to heat up before classes or when it will be turned off after classes. By planning heating and lightning, energy and costs will be saved. This paper presents data acquisition methods for preparing a dataset to create a machine learning (ML) model to save energy loss in university laboratories. The developed model will predict energy consumption in classrooms and laboratories based on weather conditions. The study is being conducted in two stages. The first stage describes how to collect data using electronic devices such as Internet of Things (IoT) controllers of Smart Building Management Systems. The second stage gave possibilities for data analysis and preparation using the phases of the Cross-Industry Standard Process for Data Mining (CRISP-DM). © 2024 IEEE.

关键词： Information management

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A Unified Modulated Model Predictive Control for a Two-Stage AC-DC Converter Interfacing a Vanadium Redox Flow Battery

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IEEE Transactions on Industry Applications 2025年

作者： Volpini, Andrea Salvo, Salvatore R. Di Tresca, Giulia Leuzzi, Riccardo Zanchetta, Pericle Electrical Computer and Biomedical Engineering Dept. Pavia Italy Department of Electrical and Information Engineering Bari Italy

With the increasing integration of renewable energy sources into electrical grids, energy storage systems have become crucial for stability and regulation. Thus, dedicated two-stage AC-DC converters are essential for integrating batteries into the grid infrastructure. These converters typically employ either single-phase or three-phase inverters paired with a Dual Active Bridge (DAB) in a back-to-back configuration, ensuring high efficiency and galvanic isolation. The conventional control scheme for such converters involves two separate PI-based control loops, one for each stage. This paper proposes a novel control strategy based on a Modulated Model Predictive Control (MMPC) algorithm for a two-stage AC-DC converter. The proposed approach utilizes a single cost-function optimization routine to simultaneously control both the grid-side and the battery-side stages, simplifying the control architecture and enhancing performance. Finally, the proposed MMPC is implemented on a DSP and FPGA-based board and the experimental results show that the proposed solution achieves faster dynamics than conventional control with no overshoots or undershoots. © 1972-2012 IEEE.

关键词： Predictive control systems

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Analysis of data from UAVs for surveillance and threat identification in maritime areas 8

Analysis of data from UAVs for surveillance and threat ident...

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8th IEEE/ACIS International Conference on Big Data, Cloud Computing, and Data Science, BCD 2023

作者： Lampropoulos, Fotis Daramouskas, Ioannis Perikos, Isidoros Paraskevas, Michael University of Peloponnese Electrical and Computer Engineering Dept. Patras Greece Hellenic Coast Guard Patras Greece University of Patras Computer Technology Institute and Press "diophantus" Computer Engineering and Informatics Department Patras Greece University of Peloponnese Computer Technology Institute and Press "diophantus" Electrical and Computer Engineering Dept. Patras Greece

ISBN: (纸本)9798350373615

In this paper, we present a methodology for drones for recognizing different types of objects in maritime areas. The concept and the aim is to assist the national maritime surveillance authorities in the identification of treats and the recognition of the exact type of the treat. The methodology relies on the use of deep learning networks and YOLO framework as well as regression models. The YOLO model detects where each object is and which label should be applied. In this way, object detection is subject to the analysis of Machine Learning-based approaches and Deep Learning-based approaches providing more information about the video or an image than traditional approaches to recognition. These approaches are used to identify groups of pixels that may individually belong to an object. This then feeds a regression model with the help of a convolutional neural network and more specifically, R-CNN, Fast R-CNN, Faster R-CNN and after that object detection algorithm approximates the location of the object and gives its label at the same time. The purpose of the application is to detect objects with an emphasis on identifying one or more targets of interest from data of a video or an image capture. An experimental study was conducted in real-world conditions and revealed quite interesting findings. © 2023 IEEE.

关键词： computer vision

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A Greek Conversational Agent for Hematologic Malignancies: Usability and User Experience Assessment

A Greek Conversational Agent for Hematologic Malignancies: U...

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34th Medical informatics Europe Conference, MIE 2024

作者： Chatzimina, Maria E. Papadaki, Helen A. Pontikoglou, Charalampos Tsiknakis, Manolis Dept. of Electrical and Computer Engineering Hellenic Mediterranean University Heraklion Greece Institute of Computer Science FORTH Heraklion Greece Dept. of Hematology School of Medicine University of Crete Heraklion Greece

ISBN: (纸本)9781643685335

Enabling patients to actively document their health information significantly improves understanding of how therapies work, disease progression, and overall life quality affects for those living with chronic disorders such as hematologic malignancies. Advancements in artificial intelligence, particularly in areas such as natural language processing and speech recognition, have resulted in the development of interactive tools tailored for healthcare. This paper introduces an innovative conversational agent tailored to the Greek language. The design and deployment of this tool, which incorporates sentiment analysis, aims at gathering detailed family histories and symptom data from individuals diagnosed with hematologic malignancies. Furthermore, we discuss the preliminary findings from a feasibility study assessing the tool's effectiveness. Initial feedback on the user experience suggests a positive reception towards the agent's usability, highlighting its potential to enhance patient engagement in a clinical setting. © 2024 The Authors.

关键词： Chatbots

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EMP-GAN: Encoder-Decoder Generative Adversarial Network for Mobility Prediction

EMP-GAN: Encoder-Decoder Generative Adversarial Network for ...

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2023 IEEE INFOCOM Conference on computer Communications Workshops, INFOCOM WKSHPS 2023

作者： Bang, Sammy Yap Xiang Raza, Syed M. Yang, Huigyu Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665494274

Ultra-dense cell deployments in Beyond 5G and 6G result in extensive overlapping between cells. This makes current reactive handover mechanism inadequate due to availability of multiple strong signals at a position. Moreover, recently proposed predictive mobility management schemes are also not suitable as they may lead to unnecessary handovers. A predictive path-based mobility management scheme can solve these issues, but forecasting User Equipment (UE) paths with high accuracy is a challenging task. This paper proposes Encoder-Decoder Generative Adversarial Network (EMP-GAN) for forecasting multi-step ahead UE path. EMP-GAN architecture consists of generator and discriminator neural networks, where the generator predicts mobility (next multi-step target sequence) and the discriminator classifies between the predicted target sequence and the ground truth in adversarial learning. Besides adversarial learning, feature matching and fact forcing training methods are employed for fast convergence of GAN and performance improvement. EMP-GAN is evaluated on mobility dataset collected from the wireless network of Pangyo ICT Research Center, Korea, and results show that it outperforms state-of-the-art prediction models. In particular, EMP-GAN achieves 95.55%, 94.70%, 93.50%, and 92.39% accuracies for 3, 5, 7, and 9-step predictions, respectively. © 2023 IEEE.

关键词： Generative adversarial networks

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Feature Pool Exploitation for Disease Detection in Fundus Images 17

Feature Pool Exploitation for Disease Detection in Fundus Im...

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17th International Conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Xiang Bang, Sammy Yap Le, Kim-Ngoc Thi Le, Duc-Tai Choo, Hyunseung Sungkyunkwan University Dept. of Superintelligence Engineering Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyunkwan University Dept. of Electrical and Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9781665453486

Retinal fundus diseases without immediate diagnoses and treatment may lead to serious consequences such as permanent visual impairment. Recently, many machine learning (ML) and deep learning (DL) models have been introduced for fundus image classification. However, those heavy models require high-end graphics processing units for training and testing, thus not suitable for real-case usage in fundus cameras with limited computation power. In this paper, we demonstrate the effectiveness of our proposed two-block model, Feature Exploitation Lightweight network (FEL-net) which consists of a Feature Exploitation Block (FEB) and Lightweight Classification Block (LCB) by comparing it with other DL models. The experiment was carried out on a dataset of 21, 697 fundus images and our model achieves 99% binary classification accuracy. The proposed robust FEB is used to generate a refined feature pool for fundus images to build an efficient ML classifier that can distinguish fundus images with disease from the normal case with high accuracy and low running time. © 2023 IEEE.

关键词： Feature extraction

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Photoacoustic Viscoelasticity Can Differentiate Tumors from Normal Tissues;A Computationally Modelling Analysis on Breast Tissue

Photoacoustic Viscoelasticity Can Differentiate Tumors from ...

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Microscopy Histopathology and Analytics, Microscopy 2024 - Part of Optica Biophotonics Congress: biomedical Optics

作者： Hosseindokht, Zahra Kolahdouz, Mohammadreza Sasanpour, Pezhman School of Electrical and Computer Engineering College of Engineering University of Tehran Tehran Iran Dept. of Medical Physics and Biomedical Engineering School of Medicine Shahid Beheshti Medical University Tehran Iran

Based on the challenges in non-invasive detection of tumors in early stages, we proposed photoacoustic viscoelasticity system and evaluated its performance computationally in COMSOL. The results revealed appropriate c... 详细信息

关键词： Noninvasive medical procedures

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Estimating the Required Resources in a Vehicular Ad Hoc Network Using Queueing Theory Models

Estimating the Required Resources in a Vehicular Ad Hoc Netw...

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2022 Panhellenic Conference on Electronics and Telecommunications, PACET 2022

作者： Keramidi, Irene Moscholios, Ioannis Logothetis, Michael Sarigiannidis, Panagiotis University of Peloponnese Dept. Informatics and Telecommuications Tripolis Greece University of Patras Dept. Electrical and Computer Engineering Patras Greece University of W. Macedonia Dept. Electrical and Computer Engineering Kozani Greece

ISBN: (数字)9798350399585

ISBN: (纸本)9798350399585

In this paper, we evaluate the performance of a Vehicular Ad Hoc Network (VANET) exploiting the M/M/c and the M(N)/M/c queueing models in order to determine the number of the resources that are required to satisfy the Quality of Service (QoS). To evaluate the system's performance we examine various quantities, such as the system's waiting time, the throughput and the number of users being served. These performance metrics are important for a VANET as they directly affect the QoS. The queueing models are employed in the following cases: a) when a vehicle communicates directly with a gNodeB (vehicle-to-infrastructure communication) and b) when a vehicle communicates with other vehicles on the road (vehicle-to-vehicle communication) in order to transmit indirectly its request to the destination gNodeB. Based on our study, we show that when the minimum possible resources are exploited, the number of users being served may be highly reduced and consequently the system's throughput can be decreased. Therefore, this condition should be adopted only when it is guaranteed that the system's overall performance will not be dangerously affected. © 2022 IEEE.

关键词： Vehicular ad hoc networks

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Classification Anterior and Posterior of Knee Osteoarthritis X-Ray Images Grade KL-2 Using Deep Learning with Random Brightness Augmentation

Classification Anterior and Posterior of Knee Osteoarthritis...

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2022 International Conference on computer engineering, Network and Intelligent Multimedia, CENIM 2022

作者： Supatman Yuniarno, Eko Mulyanto Purnomo, Mauridhi Hery Institut Teknologi Sepuluh Nopember FTI UMBY Dept. of Electrical Engineering Dept. of Informatics Surabaya60111 Indonesia Institut Teknologi Sepuluh Nopember Dept. of Electrical Engineering Dept. of Computer Engineering Surabaya60111 Indonesia

ISBN: (纸本)9781665476508

Osteoarthritis of the knee (KOA) is a narrowing of the joint space area (JSA) due to lack of fluid in the knee joint, resulting in pain when moving and when it is severe, the femur and tibia meet. Medical personnel and existing computer-based methods have been able to detect patients with X-rays but have not been able to detect where visually the posterior AJ is still wide while the anterior AJ is actually narrow, so that the patient still feels joint pain. A new approach is proposed for the classification of X-Ray Grade Kellgren-Lawrence (KL)-2 Osteoarthritis Initiative image datasets using Deep Learning Neural Networks (DCNN) by setting the Random Brightness Augmentation hyperparameter. The experimental results obtained X-Ray Grade KL-2 narrowing image classification 'Anterior View KOA' and 'Posterior View KOA' with training accuracy of 83.33% and validation accuracy of 54.69% at Random Brightness with a value of 30. © 2022 IEEE.

关键词： computer vision

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