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2024 ASU International Conference in Emerging Technologies for Sustainability and Intelligent Systems, ICETSIS 2024

作者： Abitova, Gulnara Mukashev, Daniyar Aitmukhanbetova, Elvira Rakhimzhanova, Mira Uskenbayeva, Gulzhan Kulniyazova, Korlan Binghamton University Dept Electrical Engineering and Computer Science Binghamton United States Astana It University Dept. of Computer Engineering Astana Kazakhstan L.N. Gumilyov Eurasian National University Dept. System Analysis and Management Astana Kazakhstan

ISBN: (纸本)9798350372229

The research explores the transformative impact of Artificial Intelligence in meteorology, weather forecasting. The study presented historical prediction methods overview, evolution highlighting from traditional models to advanced AI-driven systems. It delves into the integration of machine and deep learning techniques in data analysis, emphasizing technologies that significantly enhanced the accuracy and efficiency of weather predictions. Key aspects covered include the neural networks application in interpreting complex atmospheric data, the big data role in providing comprehensive training sets for AI models, the predictive analytics utilization for short-term and long-term weather forecasts. The study also examines case-study with AI implemented in weather forecasting, capability demonstrating in handling extreme weather events and climate anomalies. The study addresses the challenges and limitations faced in the meteorology AI integration, such as data quality concerns, computational requirements, and the specialized expertise need. It proposes potential solutions, future directions for research domain, suggesting a multidisciplinary approach involving meteorologists, data scientists, and AI experts. The conclusion underscores the revolutionary impact of AI-technology in meteorology, projecting how continuous advancements in AI-technology could redefine the predicting weather patterns approach. The work highlights the AI current state in weather forecasting but also sets the future innovations stage. © 2024 IEEE.

关键词： artificial intelligence data analysis machine learning neural networks predictive analytics weather Forecasting

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Multiclass Cricket Shot Classification using CNN 26

Multiclass Cricket Shot Classification using CNN

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Ahmed, Mirza Sabbir Hasan, Md Nahid Zinnah Ayon, Walid Ibn North South University Department of Electrical & Computer Engineering Dhaka Bangladesh Deggendorf Institute of Technology Departmentof Building Products and Processes Deggendorf Germany Chittagong University of Engineering & Technology Department of Computer Science & Engineering Chattogram Bangladesh

ISBN: (纸本)9798350359015

An acknowledgement of context-based advertising for the audience watching cricket can involve identifying the game of cricket based on the various batting shots, creating sensor-based commentary systems, and creating coaching assistants. This research identifies different cricket shot problems from a vast-scale cricket shot video dataset. The study uses a large dataset of annotated cricket shots that spans a variety of game formats and captures different shot types and player styles. Some cricket shots are the action we find most attractive in cricket videos. Those shots are often captured by cameras installed at the cricket ground's stands at both ends of the cricket pitch. To identify 4 different cricket batting shots from offline footage, this research paper presents a novel approach to classify cricket shots based on deep learning techniques. In this research, we propose a multi-stage classification pipeline for cricket shots, which combines dimensionality reduction, feature extraction, and classification techniques of cricket shots. Before classification, we have preprocessed the dataset that we prepared. The image data was extracted from the video containing around 3000 image frames. The dataset has four classes: i.e. pull shot, square drive, straight drive, and uppercut. The preprocessing steps are normalization, resize, and histogram equalization. After that a scratch model is designed and the model is trained based on the architecture of the scratch model. Finally, we have found the model's accuracy for our prepared dataset is 98.18 percent. © 2023 IEEE.

关键词： batting shots classification convolutional neural network deep learning

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Two-Stage Piezoelectric Resonator / Switched Capacitor DC-DC Converter

Two-Stage Piezoelectric Resonator / Switched Capacitor DC-DC...

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2023 IEEE Energy Conversion Congress and Exposition, ECCE 2023

作者： Wanyeki, Babuabel M. Boles, Jessica D. Lang, Jeffery H. Perreault, David J. Meta Platforms Inc New York CityNY United States University of California Berkeley Electrical Engineering and Computer Sciences BerkeleyCA United States Massachusetts Institute of Technology Electrical Engineering and Computer Science CambridgeMA United States

ISBN: (纸本)9798350316445

Magnetics pose a significant challenge for power converter miniaturization. Approaches to address this include power converters based on piezoelectric resonators (PRs) rather than magnetics, and switched-capacitor (SC) converters which utilize only capacitors. However, PR-based converter efficiencies tend to decrease at high voltage conversion ratios, and purely SC-based converters are not capable of efficiently regulating voltage. Through the hybridization of PR and SC-based converters, there is an opportunity for each to address the drawbacks of the other to create a high power density, high efficiency power converter for applications requiring high voltage conversion ratios and wide, continuous voltage conversion ranges. In this paper, we propose a two-stage dc-dc converter with a PR-based regulation stage and a SC-based transformation stage. We detail a new simplified eight-stage switching sequence for the PR stage and experimentally validate this concept in a prototype converter. © 2023 IEEE.

关键词： dc-dc power converters piezoelectric resonators switched capacitor converters switching sequences

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Multi-level Learning for Semi-supervised Multi-view Classification 11

Multi-level Learning for Semi-supervised Multi-view Classifi...

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11th International Conference on Advanced Cloud and Big Data, CBD 2023

作者： Song, Na Yang, Shundong Cai, Zhiling Lin, Jian Huang, Sujia Putian University School of Mechanical Electrical and Information Engineering Putian China Hainan University School of Computer Science and Technology Haikou China Fujian Agriculture and Forestry University College of Computer and Information Science Fuzhou China Fuzhou University College of Computer and Data Science Fuzhou China

ISBN: (纸本)9798350345346

Multi-view semi-supervised classification primarily aims to enhance classification accuracy when dealing with limited labeled samples. Although existing methods have shown impressive performance, significant challenges still persist in efficiently propagating label information and capturing global relationship information within multi-view data. To address the aforementioned challenges, a Graph Convolutional Network (GCN)-based framework is presented, which explores the acquisition of multilevel representations, encompassing feature-level representations and the fusion of global structural associations. The state-of-the-art performance of the proposed model is unequivocally supported by a comprehensive set of empirical results. © 2023 IEEE.

关键词： computer vision

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Designing an IT2 Fuzzy Rule-Based System for Emotion Recognition Using Biological Data

Designing an IT2 Fuzzy Rule-Based System for Emotion Recogni...

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International eConference on computer and Knowledge engineering (ICCKE)

作者： Mahsa Keshtkar Hooman Tahayori Department of Computer Science and Engineering & IT School of Electrical and Computer Engineering Shiraz University Shiraz Iran

ISBN: (数字)9798331511272

ISBN: (纸本)9798331511289

In recent years, significant efforts have been dedicated to detect human emotions. This interest primarily stems from the fact that emotions influence individuals' reactions and behaviors. Understanding these impacts can be beneficial in various fields, ranging from developing healthcare surveillance systems for the elderly to identifying adverse emotions in those engaged in high-stakes occupations, such as pilots, sailors, and long-haul truckers. This paper presents a solution based on the application of interval type two fuzzy sets and systems to detect individuals' emotions using their physiological signals. The physiological signals on the same emotion varies from person to person and even for the same person in different situations. This variability has led us to employ interval type-2 fuzzy sets. We defined five footprints of uncertainty for each physiological signal to cover its whole range and employed instance selection technique to optimize the rule base. We develop a rule-based fuzzy system that employs the fewest feasible number of rules for the purpose of emotion recognition. In order to enhance the comprehensibility of the system, we have also utilized a minimal set of features in the construction of the rules. This study implemented on physiological signals from the K-EmoCon dataset. Obtained results shows that the proposed method outperform other existing method with lower computational complexity.

关键词： Knowledge engineering Emotion recognition Fuzzy sets Uncertainty Surveillance Medical services Physiology Distance measurement Older adults Fuzzy systems

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Circumventing Google Play vetting policies: a stealthy cyberattack that uses incremental updates to breach privacy

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Journal of Ambient Intelligence and Humanized Computing 2023年第5期14卷 4785-4794页

作者： Muhammad, Zia Amjad, Faisal Iqbal, Zafar Javed, Abdul Rehman Gadekallu, Thippa Reddy Department of Computer Science North Dakota State University Fargo United States Department of Cyber Security PAF Complex E-9 Air University Islamabad Pakistan Islamabad Pakistan School of Information Technology and Engineering Vellore Institute of Technology Vellore India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

Today digital technologies are evolving to accommodate small businesses and young entrepreneurs by reducing their time-to-market while encouraging rapid innovation in mobile, Extended Reality (XR), Internet of Things (IoT), cloud, and edge devices. The leading operating system Android typically takes one to a few days to perform application vetting and go to production by leveraging code analysis technologies in their Play Protect anti-malware program. However, developers with malicious intent are looking to circumvent this detection mechanism by exploiting Google’s relatively lenient trust policies that allow for package distribution and feature updates. This paper develops a proof-of-concept malware that exploits customers’ trust and Google’s policies to circumvent popular voice search applications. Our results show that attackers can initially circumvent Play Protect by uploading benign applications to build trust and then add malicious feature updates incrementally to distribute highly intrusive malware into user systems. This malware can scan and collect private user data from the device and exfiltrate it to the command-and-control server. The contributions are three-fold. (1) A proof-of-concept stealthy malware and publishing mechanism has developed that highlights the relative ease with which Google Play Protect policies may be subverted. (2) a comprehensive evaluation has been performed using major publicly available anti-malware solutions. (3) Recommendations and policies have been suggested to prevent this attack and ensure users’ privacy concerns (IMUTA is a novel attack in which malicious functionality is slowly added to a benign application through updates. This attack evades malware detection tools and exploits user trust. The attack can be launched against any application distribution platform like the Play Store). © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Mobile security

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Impact of Parametric Uncertainties on the Steady-State Performances of DFIG-Based WECS 10th

Impact of Parametric Uncertainties on the Steady-State Perfo...

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10th EAI International Conference on Advancements of science and Technology, EAI ICAST 2022

作者： Ayenew, Endalew Biru, Getachew Mulatu, Asrat Berhanu, Milkias Electrical Power and Control Engineering School of Electrical Engineering and Computing Adama Science and Technology University Adama Ethiopia School of Electrical and Computer Engineering Addis Ababa Institute of Technology Addis Ababa University Addis Ababa Ethiopia Center of Excellence for Sustainable Energy College of Electrical and Mechanical Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia

ISBN: (纸本)9783031411724

This chapter presents the study carried out to discover uncertain parameters in a model of a doubly-fed induction generator (DFIG)-based wind energy conversion system (WECS) and to investigate their effects on WECS plant while harvesting energy from wind at varying speeds. This is carried out by analytically formulating the steady-state models of a DFIG-based WECS and computing the parameters of these models at different wind speeds that drive wind turbines at rotor speed from sub-synchronous to super-synchronous. The main uncertain parameters in this system are the mutual reactances between stator and rotor windings, and the reactance of rotor winding. These variations happen due to the varying wind speed. The effect of parametric uncertainties on the steady-state performances of DFIG-based WECS is examined. The results depicted that the steady-state performances of DFIG-based WECS are influenced by uncertain phenomena. Typically a 0.5% change in mutual reactances caused up to 10 kW change in the generator output power. Mainly, it causes instability in the performances of the DFIG-based WECS and slightly reduces overall efficiency at a higher value of the wind speed. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： DFIG-based WECS HAWT Performances of WECS Uncertain parameter

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A Robust Security Method for Medical Image Based on 2D Schaffer Map 4

A Robust Security Method for Medical Image Based on 2D Schaf...

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4th IEEE International Conference on electrical, computer, and Energy Technologies, ICECET 2024

作者： Sharma, Suvita Rani Kaur, Manpreet Singh, Birmohan Minocha, Sachin Sant Longowal Institute of Engineering and Technology Department of Computer Science and Engineering Sangrur Punjab Longowal148106 India Bennett University SCSET Greater Noida India Sant Longowal Institute of Engineering and Technology Department of Electrical and Instrumentation Engineering Sangrur Punjab Longowal148106 India School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (纸本)9798350395914

In this work, a methodology has been propounded for the security of the medical images containing Confusion and Diffusion phases in which a 2D Schaffer map is utilized. The confusion phase rearranges the row and column of medical images using the sorted indexes of the chaotic sequences produced by the Schaffer map. During the diffusion phase, the chaotic sequences are utilized to alter the pixel values of the original image through the XOR operation. This phase applies to both the row and column pixel values of the images, enhancing security. The efficacy of the proposed security method has been assessed through histogram analysis, chi-square, and variance test, revealing uniformity in encrypted image pixel values. The correlation analysis shows results closer to zero and an entropy value nearly equal to the ideal 8. Moreover, the encrypted image exhibits lower Peak Signal-to-Noise Ratio (PSNR) and higher Mean Square Error (MSE) values, indicating structural dissimilarity with the original image. The Unified Averaged Changed Intensity (UACI) value exceeds 33.40% and the Number of Changing Pixel Rate (NPCR) value exceeds 99.60%, indicating resistance against differential attacks, while the Structural Similarity Measure (SSIM) values closer to zero for the encrypted image indicates structural dissimilarity. The results affirm that the propounded methodology is secure from entropy, differential, statistical, and brute force attacks. © 2024 IEEE.

关键词： Image enhancement

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Building the metaverse using digital twins at all scales,states,and relations

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Virtual Reality & Intelligent Hardware 2022年第6期4卷 459-470页

作者： Zhihan LV Shuxuan XIE Yuxi LI M.Shamim HOSSAIN Abdulmotaleb El SADDIK Department of Game Design Faculty of ArtsUppsala UniversitySweden College of Computer Science and Technology Qingdao UniversityQingdaoChina Department of Software Engineering College of Computer and Information SciencesKing Saud UniversityRiyadhSaudi Arabia School of Electrical Engineering and Computer Science(EECS) University of OttawaOttawaOntarioCanada

Developments in new-generation information technology have enabled Digital Twins to reshape the physical world into a virtual digital space and provide technical support for constructing the *** objects can be at the micro-,meso-,or *** Metaverse is a complex collection of solid,liquid,gaseous,plasma,and other uncertain ***,the Metaverse integrates tangibles with social relations,such as interpersonal(friends,partners,and family)and social relations(ethics,morality,and law).This review introduces some principles and laws,such as broken windows theory,small-world phenomenon,survivor bias,and herd behavior,for constructing a Digital Twins model for social ***,from multiple perspectives,this article reviews mappings of tangible and intangible real-world objects to the Metaverse using the Digital Twins model.

关键词： Digital twins Metaverse Sensor

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COMPARATIVE STUDY OF MECHANICAL PERFORMANCE OF MICROWAVE-CURED AND AUTOCLAVE-CURED CARBON/EPOXY COMPOSITES 10

COMPARATIVE STUDY OF MECHANICAL PERFORMANCE OF MICROWAVE-CUR...

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10th Composites and Advanced Materials Expo, CAMX 2024

作者： Pundhir, N. Heskin, M. Luo, W. Chandrashekhara, K. Wilcox, L. Donnell, K. Department of Mechanical and Aerospace Engineering Missouri University of Science and Technology RollaMO65409 United States Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO65409 United States

ISBN: (纸本)9781934551462

The unique combination of properties offered by carbon fiber-reinforced polymer composites has led to their widespread adoption in aerospace, automotive, and other industries due to their high specific stiffness and strength properties. The present study investigates the potential of vacuum-assisted microwave curing for carbon fiber-reinforced polymer composites which presents several advantages over traditional autoclave curing methods. Microwave curing offers reduced cycle times, volumetric heating, enhanced safety, and lower energy consumption. This comprehensive approach evaluates the feasibility and efficacy of vacuum-assisted microwave curing for carbon fiber-reinforced polymer composites by comparing the manufactured composites against traditional autoclave composites. Mechanical properties and microstructural characteristics were measured and examined to accomplish this. In the present work, industrially available IM7/Cycom 5320-1 unidirectional prepreg with 33% resin by weight and a fiber area weight of 145 g/m2 is employed to manufacture the laminated composite. A sixteen-layer symmetric cross-ply ([00/900]4s) and quasi-isotropic ([450/900/-450/00]2s) laminated composites are manufactured using a custom-built microwave curing chamber. Mechanical characterization of the microwave-cured composite is conducted through uniaxial tensile and flexural tests, with subsequent comparison to autoclave-cured samples. Optical microscopy is employed to estimate the quality of the manufactured samples. The results obtained from this study support advancements in composite manufacturing processes, particularly in the aerospace and automotive sectors. Copyright 2024. Used by CAMX – The Composites and Advanced Materials Expo.

关键词： Laminated composites

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