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8th Southern Power Electronics Conference and the 17th Brazilian Power Electronics Conference, SPEC / COBEP 2023

作者： López-Santos, Oswaldo Flores-Bahamonde, F. Torres-Pinzón, C.A. Haroun, Reham Valderrama-Blavi, Hugo Garriga, Juan A. Martínez-Salamero, Luis Rovira i Virgili University Group of Automatic Control and Industrial Electronics Tarragona Spain Universidad Andres Bello Energy Transformation Center Faculty of Enigineering Chile Universidad Santo Tomás Faculty of Electronic Engineering Colombia University of Lleida Department of Computer Science and Industrial Engineering Lleida Spain

ISBN: (纸本)9798350321128

In this paper, the quadratic buck converter (QBC) is proposed as competitive alternative to implement a battery charger. Since, QBC is a high order system, the required control is designed to follow the conventional constant-current constant-voltage regime by means of three loops. Namely, i) an inner-loop operating in sliding mode to control the current of the closest inductor to the input port providing the properly stability of the system, ii) a first outer loop designed to regulate the battery voltage providing the reference of the inner loop, and finally iii) a second outer loop to regulate the battery current modifying the reference of the voltage loop. Proportional Integral (PI) controllers are used in both outer loops. Simulation results are presented validating the theoretical study. © 2023 IEEE.

关键词： Battery charger Quadratic Buck Converter Sliding Mode Control

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Enhancing Urban Road Safety: A 5G NR Model for Vulnerable Road Users Awareness

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IEEE Access 2025年 13卷 99170-99182页

作者： Yáñez, Alexis Salas, Felipe Azurdia-Meza, César Sandoval, Jorge Céspedes, Sandra Concordia University Gina Cody School of Engineering and Computer Science Department of Computer Science & Software Engineering Montreal H3G 1M8 QC Canada University of Chile Faculty of Physical and Mathematical Sciences Department of Electrical Engineering Santiago 8370451 Chile Bain & Company Inc. Boston MA United States

The widespread adoption of cellular networks has played an important role in the integration of road safety applications, capitalizing on the ubiquitous use of this technology by road users and its extensive market penetration. This paper presents the formulation of an analytical model to assess the awareness of vulnerable road users (VRU) defined as VRU Awareness Probability (VAP) achieved by vehicles communicating via 5G New Radio (NR) cellular vehicle-to-everything C-V2X networks at urban intersections. The main assumption in our model is that all VRU are connected to a 5G network. Our analytical model is designed to represent the dynamics of the 5G NR network operating in communication mode 2 in an urban intersection setting;we consider the influencing factors of signal propagation, such as obstacles created by buildings and realistic mobility patterns for vehicles and VRU based on SUMO traces. Our research shows that the proposed model exhibits high accuracy in intersection scenarios without buildings and maintains this efficiency in partially and highly obstructed scenarios, showing a Root Mean Square Error (RMSE) of 0.0265 and 0.0309, respectively;using the fitted model as a baseline in the non-obstructed scenario. The efficiency of this model can facilitate the study of the impact of the implementation and development of road safety applications on the safety of VRU. This VRU-centered approach allows for evaluating VRU awareness in different traffic density contexts, communication network conditions, and architectures where safety applications can be deployed. © 2013 IEEE.

关键词： Autonomous Resource Allocation Cellular Vehicle-to-everything (C-V2X) New Radio (NR) Semi-persistent Scheduling (SPS) Vulnerable Road Users (VRU)

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Advanced Power and Voltage Regulation in a DC Microgrid with Solar Energy and Battery Storage

Advanced Power and Voltage Regulation in a DC Microgrid with...

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International Renewable Energy Congress

作者： M. Elrashid Z. Rajab M. Elhashane A. Younis J. Gómez-Romero Salah I.S Tnatin F. Mohamed Electrical and Electronic Department Faculty if Engineering Benghazi University Department of Computer Science and Artificial Intelligence University of Granada Electrical Department Faculty of Engineering Omar Al-Mukhtar University Libya Authority of Natural Science Research and Technology Tripoli Libya

ISBN: (数字)9798331516772

ISBN: (纸本)9798331516789

This research investigates the modeling, design, and simulation of a microgrid with advanced energy management controller, developed using MATLAB/Simulink. This paper presents a comprehensive analysis, modeling, and simulation of a smart controller for a boost converter, using an artificial neural network (ANN) for maximum power point tracking (MPPT) to enhance power output. The study begins with a comparison between traditional MPPT techniques and the ANN-based approach. These MPPT strategies are compared in terms of their response to dynamic weather conditions (such as temperature and irradiance variations) and their ability to minimize oscillations around the maximum power point. The simulation results demonstrate that the ANN-based MPPT method outperforms the conventional P&O and IC methods, providing faster response times and higher output power. Additionally, the system's performance was evaluated under conditions of power shortage and during battery charging and discharging cycles, showing the effectiveness of the smart controller in maintaining system stability and optimizing energy management.

关键词： Maximum power point trackers Renewable energy sources Microgrids Artificial neural networks Batteries Time factors MATLAB Energy management Voltage control Thermal stability

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Data Mining and Bioinformatics Approaches to Elucidate Bacterial Communities in the Extreme Environment

Data Mining and Bioinformatics Approaches to Elucidate Bacte...

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2023 International Conference on Informatics, Multimedia, Cyber and Information Systems, ICIMCIS 2023

作者： Trinugroho, Joko Pebrianto Hidayat, Alam Ahmad Nirwantono, Rudi Asadi, Faisal Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Mathematics Department Jakarta11480 Indonesia Bina Nusantara University Faculty of Engineering Bioinformatics and Data Science Research Center Biotechnology Department Jakarta11480 Indonesia Bina Nusantara University School of Computer Science Bioinformatics and Data Science Research Center Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798350307306

Bacteria are microscopic organisms that can be found in many environments. They are abundant and have many roles in our life. Studying bacteria is essential so that we can identify the bacteria that are needed for many industrial applications. However, the main problem is that majority of the bacteria are unculturable, hampering the exploration of bacteria from different environments. Metagenomics approach which employs Next Generation Sequencing technology could help study bacteria by utilizing 16S rRNA marker gene. This study aims to demonstrate data mining and bioinformatics approaches to analyze 16S rRNA sequencing data. The raw sequencing data of 16S rRNA was collected from biological database. Then, the data were trimmed, denoised, and clustered to generate Amplicon Sequence Variants (ASVs). Diversity (alpha and beta) and taxonomic analyses were then conducted to elucidate the bacterial diversity and taxonomic profile of ASVs. The results of this work showed that Shannon and PD indices in China's hot spring were higher than Singapore and the USA. Furthermore, a significant difference was observed in the PD index. The unweighted UniFrac distance also showed there was a significant difference of the bacterial communities between the three locations. In addition, the taxonomic investigation unveiled prevalent bacterial groups in the ecosystem, namely Proteobacteria, Chloroflexi, Cyanobacteria, and Crenarchaeota. The research outcomes have the potential to serve as a foundational resource for subsequent bacterial metagenomic research, particularly the hot spring environment. © 2023 IEEE.

关键词： Bacteria

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Bioinformatics Analysis of Oral Squamous Cell Carcinomas and Their Interaction to Identify Molecular Signatures 1st

Bioinformatics Analysis of Oral Squamous Cell Carcinomas and...

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1st International Symposium on Artificial Intelligence, ISAI 2022

作者： Sarker, Bandhan Rahaman, Md. Matiur Khan, Suman Sinha, Jayashri Deb Barman, Subhabrata Department of Statistics Faculty of Science Bangabandhu Sheikh Mujibur Rahman Science and Technology University Gopalganj-8100 Dhaka Bangladesh Department of Computer Science and Engineering Bengal Institute of Technology and Management West Bengal Santiniketan721657 India Department of Computer Science and Engineering Haldia Institute of Technology West Bengal Haldia721657 India

ISBN: (纸本)9783031224843

Squamous epithelium is the origin of solid tumor oral squamous cell carcinoma (OSCC). Every year, nearly 400,000 OSCC patients are added to the cancer database. Presently, chemo-radiotherapy is the main important adjuvant treatment for OSCC;nevertheless, clinical resistance (drug resistance) to chemotherapy still leads to a poor prognosis of OSCC patients. Identification of potential genes and drugs might be a significant lead for the analyses of OSCC research. We aimed to identify molecular signatures for the diagnosis of OSCC patients. Statistical methods (ANOVA, limma and SAM) were used to identify differentially expressed genes (DEGs) from two datasets GSE111585 and GSE115119. Considering the cutoff values less than 0.05 and |log FC| greater than 1, we obtained 27 up-regulated and 25 down-regulated common DEGs. Protein-protein interaction (PPI) network determined hub genes (AR, ETS1, MET, PDGFB and VAV3) using STRING database. Other biomolecules: Reporter transcription factors (HIF1A, MYC, FOXP3, E2F4, WT1, PURA, ZEB1 and USF2), microRNAs (hsa-miR-589-3p, hsa-miR-155-5p and hsa-miR-301b-3p) associated with hub-genes were determined. For the hub genes, we also performed GO and KEGG enrichment analysis. We constructed gene-drug interaction using the DGIdb database and identify targeted drugs (IMATINIB, BROMOCRIPTINE, NIFEDIPINE, PIRETANIDE and TRIAMTERENE) for OSCC. Our study suggested that the biomarkers (hub-genes, TFs and miRNAs) and the discovered drugs might be a therapeutic target of OSCC. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Transcription factors

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Have Java Production Methods Co-Evolved With Test Methods Properly?: A Fine-Grained Repository-Based Co-Evolution Analysis 48

Have Java Production Methods Co-Evolved With Test Methods Pr...

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48th Euromicro Conference on Software engineering and Advanced Applications, SEAA 2022

作者： Kita, Tenma Aman, Hirohisa Amasaki, Sousuke Yokogawa, Tomoyuki Kawahara, Minoru Department of Computer Science Faculty of Engineering Ehime University Matsuyama Ehime790–8577 Japan Center for Information Technology Ehime University Matsuyama Ehime790–8577 Japan Faculty of Computer Science and Systems Engineering Okayama Prefectural University Soja Okayama719–1197 Japan

ISBN: (纸本)9781665461528

Any source code of a software product (production code) is expected to be tested to ensure its correct behavior. Whenever a developer updates production code, the developer should also update or create the corresponding test code to check if the updated parts still work correctly. Such a desirable co-evolution relationship between production and test code forms a logical coupling. Although the logical coupling is detectable through an association analysis on the code repository such as Git, the detection granularity is coarse because the conventional repository is at the file level. For observing those logical couplings as precisely as possible, this paper utilizes the finer-grained, Java method-level repository (FinerGit). Then the paper proposes a metric measuring the extent to which a production method has co-evolved with test methods and conducts a case study using ten open-source projects. The results show that most Java methods (98% on average) have co-evolved with test methods, but some have not;The proposed metric helps detect those methods having the potential risk that the developers might not test adequately. © 2022 IEEE.

关键词： Software testing

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AdaptVR: An Adaptive Learning VR System for Dental Students Training

AdaptVR: An Adaptive Learning VR System for Dental Students ...

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International Conference on computer and Applications (ICCA)

作者： Meriam Sherif Nahla Barakat Abeer Hamdy Department of Software Engineering Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt Department of Artificial Intelligence Faculty of Informatics and Computer Science The British University in Egypt Cairo Egypt

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

The paper proposes “AdaptVR” a virtual reality (VR) system designed to enhance dental training through realistic real tile simulations and adaptive learning environment, to overcome traditional training challenges in dental education. The system aims to provide personalized learning experiences, facilitating skill development and comprehensive assessments for students. The proposed system was tested by 17 dentistry students and 5 experts. AdaptVR framework is done for operative dentistry which is removing decay especially Class I and Class II of classification of caries. Results from the experiment revealed that 77.3% of participants exceed their expectations, 59.1% saw a positive effect on their dental skills, 90.9% reported higher levels of engagement, and 65% indicated that vibration and force feedback provided students with a genuine sensation. AdpatVR's experimental findings show an average accuracy of 50.85% for Class I, 51.7% for Class II, and 82.09% for the Class II box technique. The error percentage averages 49% for Class I, 48% for Class II, and 18% for Class II box technique.

关键词： Training Vibrations Drilling Solid modeling Adaptive learning Adaptation models Force feedback Virtual reality Teeth Dentistry

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Electronic Structure and Hole Concentration of Ce1-xSrxNiO2 and Nd1-xSrxNiO2 by Density Functional Theory 1

Electronic Structure and Hole Concentration of Ce1-xSrxNiO2 ...

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1st International Conference on Electronic and computer engineering, ECE 2023

作者： Cheong, Choon Min Chen, Soo Kien Faculty of Computer Science & Information Technology Universiti Malaya Kuala Lumpur57100 Malaysia School of Engineering INTI International College Penang Penang11900 Malaysia Department of Physics Faculty of Science Universiti Putra Malaysia Selangor Darul Ehsan Serdang43300 Malaysia

Recently, superconductivity was discovered in nickelate Nd0.8Sr0.2NiO2. It is predicted to be a cuprate-like superconductor. Cerium, with its natural oxidation states 3+ and 4+, has a stronger effect on the modification of the Ni electronic structure near the Fermi level than neodymium. This work investigates the electronic states and the charge distribution of CeNiO2 and NdNiO2 compounds. Hole concentration and holes doped relative to the parent compound of Ce1-xSrxNiO2 and Nd1-xSrxNiO2 with x = 0, 0.2, 0.33 are calculated. The results shown that the Ni ion in NdNiO2 have half-filled 3dx2-y2 orbital with ionic charge of Ni+, while Ni ion in CeNiO2 is in mixed charges of Ni+ and Ni2+. The undoped CeNiO2 has almost the same hole concentration as that of 20% Sr-doped NdNiO2 © 2023 Institute of Physics Publishing. All rights reserved.

关键词： Density functional theory

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Leveraging Machine Learning for Crop Disease Identification: Comparing CNN and SVM Models

Leveraging Machine Learning for Crop Disease Identification:...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Maahi Shah Kruti Sakariya Dipak Ramoliya Computer Science & Engineering Department Faculty of Technology and Engineering (FTE) Devang Patel Institute of Advance Technology and Research (DEPSTAR) Charotar University of Science and Technology (CHARUSAT) Anand India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Crops form a key part of the food cycle. But, within this vast and complex agriculture domain, the crops have their predators. Crop diseases may cause revenue losses to farmers and jeopardize food security. These detractors, without a doubt, can destroy the productivity and even the quality of crops, creating a definite concern for the economic sustenance of the farmers and food security from a global perspective. The spread of crop diseases has increased in recent decades due to agricultural intensification, globalization, trade, and climate change. For some reasonable circumstances, these farmers do not know the cause of the disease. Thus, there needs to be a valid mechanism through which such farmers can be assisted in diagnosing the crop disease. Henceforth, we have created a machine learning that examines crop leaves to identify agricultural diseases to ease their work. The Implementation of this was done using two algorithms; Convolutional neural Network (CNN) and Support Vector Machine (SVM). Comparing the two algorithms to determine which is more important for picture categorization is the aim. This study offers a thorough analysis of CNN and SVM algorithms used in agricultural disease detection, with a particular emphasis on fruit and vegetable leaf identification. Using the PlantVillage dataset, it tests these two machine-learning models in multiple implementations. CNN achieved an accuracy of 75.76 % for fruit leaves and 84.38% for vegetable leaves, whereas SVM showed an accuracy of 75.00% for fruit leaves and 75.11% for vegetable leaves. from the above comparison, it can be seen that CNN acts better than SVM, and more so in cases when their feature representations are quite complex. This paper is intended to provide the reader with an overview of how these techniques can best be applied for real-world applications in precision agriculture.

关键词： Support vector machines Precision agriculture Accuracy Machine learning algorithms Crops Food security Machine learning Feature extraction Convolutional neural networks Diseases

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Transforming Education with Data Mining: Opportunities, Applications, and Challenges

Transforming Education with Data Mining: Opportunities, Appl...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Amey Raut Shital Hajare Department of Computer Science & Engineering Faculty of Engineering & Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Educational Data Mining (EDM), a scientific subject that emphasizes employing data analysis to improve instruction and learning, was created because of the increased usage of data in education. Through the application of predictive analytics, clustering, and personalized learning systems EDM helps the educators to understand the student performance, interest and requirement. Identifying problem areas that require attention, allocating resources as efficiently as feasible, and developing learning experiences that are customized to each student's needs are all made possible by the data generated by EDM. Although EDM has the power to change the face of education for the better, there are issues such as data privacy, ethical issues and absence of guidelines that still pose a threat. These challenges may be hailed as future advancements in the fields of artificial intelligence, blockchain and other related technologies could also solve these challenges. With the growth of the digital education EDM is likely to transform the way teaching and learning is done by making it more customized, barrier free and effective in the near future.

关键词： Learning systems Ethics Data privacy Technological innovation Shape Education Neural networks Transforms Blockchains Predictive analytics

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