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2024 IEEE International Conference on Intelligent systems and Advanced Applications, ICISAA 2024

作者： Mishra, Ajit Kumar Verma, Vikash Manivannan, R. Kumar, N. Krishna Srividya, Mudunuru Sharma, Sanskriti IES College of Technology Department of Electrical & Electronics Engineering M.P Bhopal462044 India GGITS Dept of Artificial Intelligence & Machine Learning M.P Jabalpur India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology School of Computing Computer Science & Engineering TN Chennai India Guru Nanak Institutions Technical Campus Ibrahimpatam Dept.of CSE Telangana Hyderabad India MLR Institute of Technology Dept. of CSE Telangana Hyderabad India GGITS Dept of Computer Science & Business System M.P Jabalpur India

ISBN: (纸本)9798331539948

Modern artificial intelligence uses geographical data mining and regional analysis to analyze large datasets. Deep learning is a new geographic AI method. Example: Geographic Deep Mining Network (GDMN). The Spatial Feature Extractor (SFE), Temporal Geospatial Attention Mechanism (TGAM), and Geospatial Data Fusion Module (GDFM) comprise this technique. We examined six popular approaches and found GDMN performed best. It worked successfully for several reasons. GDMN always outperforms its competitors in memory, F1 score, IoU, training time, and inference time. This is always true. These findings suggest that GDMN might boost geospatial AI's data mining and location analysis abilities. After all the effort, we have developed a solid basis for location intelligence, making it useful in various subject areas. Because it's versatile, mapping specialists may use GDMN for better environmental monitoring or city development. Deep learning helps GDMN make better space choices and uncover insights in large, challenging data sets that no one knew about. GDMN's cutting-edge approaches make space and time research and prediction simpler. This wide perspective offers a more full and all-encompassing picture of today's challenges than previous techniques. This makes it impossible to overstate how significant this work is to location intelligence. Its unmatched research and insights usher in a new era. © 2024 IEEE.

关键词： Adversarial machine learning

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The effect of extended kit-build concept mapping on students' perceptions 7

The effect of extended kit-build concept mapping on students...

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7th International Conference on Education and Technology, ICET 2021

作者： Prasetya, Didik Dwi Pinandito, Aryo Hayashi, Yusuke Hirashima, Tsukasa Universitas Negeri Malang Dept. of Electrical Engineering Malang Indonesia Universitas Brawijaya Faculty of Computer Science Information System Department Malang Indonesia Graduate School of Engineering Hiroshima University Dept. of Information Engineering Higashihiroshima Japan

ISBN: (纸本)9781665427814

Extension concept mapping is a technique to connect prior existing concept maps with a relevant new knowledge structure. It provides learners with the opportunity to review initial ideas, eliciting missing relationships, adding new components, and improve knowledge structure. While the current study proposed that the Extended Kit-Build design promotes students' achievements, it has yet to evaluate the students' perception. This study investigated the effects of Extended Kit-Build concept mapping design on students' perceptions using the Technology Acceptance Model. The perceived ease-of-use, perceived usefulness, and Behavioral Intention constructs were used to investigate users' intention to use the Extended Kit-Build tool in learning. The results reported that the learners perceived positive effects on Extended Kit-Build as ease of use and a useful learning tool. They also have a high intention to use the tool in the future. © 2021 IEEE.

关键词： Students

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ForgeEDA: A Comprehensive Multimodal Dataset for Advancing EDA

arXiv

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arXiv 2025年

作者： Shi, Zhengyuan Li, Zeju Ma, Chengyu Zhou, Yunhao Zheng, Ziyang Liu, Jiawei Pan, Hongyang Zhou, Lingfeng Li, Kezhi Zhu, Jiaying Yan, Lingwei He, Zhiqiang Xue, Chenhao Jiang, Wentao Yang, Fan Sun, Guangyu Yang, Xiaoyan Chen, Gang Shi, Chuan Chu, Zhufei Yang, Jun Xu, Qiang Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China School of Computer Science Beijing University of Posts and Telecommunications Beijing China School of Microelectronics State Key Laboratory of Integrated Chips and System Fudan University Shanghai China School of Electronic and Information Engineering Hangzhou Dianzi University Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Integrated Circuits Peking University Beijing China School of Intergrated Circuits Southeast University Nanjing China National Center of Technology Innovation for EDA Nanjing China

We introduce ForgeEDA, an open-source comprehensive circuit dataset across various categories. ForgeEDA includes diverse circuit representations such as Register Transfer Level (RTL) code, Post-mapping (PM) netlists, And-Inverter Graphs (AIGs), and placed netlists, enabling comprehensive analysis and development. We demonstrate ForgeEDA’s utility by benchmarking state-of-the-art EDA algorithms on critical tasks such as Power, Performance, and Area (PPA) optimization, highlighting its ability to expose performance gaps and drive advancements. Additionally, ForgeEDA’s scale and diversity facilitate the training of AI models for EDA tasks, demonstrating its potential to improve model performance and generalization. By addressing limitations in existing datasets, ForgeEDA aims to catalyze breakthroughs in modern IC design and support the next generation of innovations in EDA. © 2025, CC BY.

关键词： Open source software

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Developing an Automatic Approach to Generate Brick Model from Building Information Modelling

SSRN

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SSRN 2024年

作者： Li, Mingchen Wang, Zhe Fierro, Gabe Cecil, Man Chi Hou Patrick, So Pok Man Calvin, Leung Kin Fung Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong Department of Computer Science Colorado School of Mines GoldenCO United States Hensen System Engineering Ltd. Hong Kong Electrical and Mechanical Services Department Government of the HKSAR Hong Kong

In the AECO (Architecture, engineering, Construction, and Operations) sector, addressing information silos for efficient building operation and maintenance is crucial. The Graph-based BIM (Building Information Modeling) supports information sharing and digital twin implementation but struggles to integrate operational data. The Brick ontology overcomes this by standardizing metadata for building assets and their relationships. However, errors in human-generated BIM models hinder widespread application. This paper introduces a novel BIM-to-Brick methodology that corrects IFC (Industry Foundation Classes) models, enabling the creation of high-quality Brick models from imperfect BIM ones. This method facilitates end-to-end digital twin creation with operational data integration, significantly reducing the effort by managing real-world BIM inaccuracies. Our case study confirms the method's effectiveness in generating compliant Brick models, showcasing its potential to improve information reusability and reduce information silos in construction. © 2024, The Authors. All rights reserved.

关键词： Data integration

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Automated Classification of Societal Sentiments on Twitter With Machine Learning

IEEE Transactions on Technology and Society

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IEEE Transactions on Technology and Society 2022年第2期3卷 100-110页

作者： Vyas, Piyush Reisslein, Martin Rimal, Bhaskar Prasad Vyas, Gitika Basyal, Ganga Prasad Muzumdar, Prathamesh Dakota State University College of Business and Information System MadisonSD57042 United States Arizona State University School of Electrical Computer and Energy Engineering TempeAZ85287 United States Dakota State University Beacom College of Computer and Cyber Sciences MadisonSD57042 United States Rajasthan Technical University Department of Renewable Energy Kota324009 India University of South Florida Muma College of Business TampaFL33613 United States

The rapid growth in information sharing on social media has defined a new information age in our society. Microblogging sites, such as Twitter, gained immense popularity during the COVID-19 pandemic. We developed an automated framework to extract the positive, negative, and neutral sentiments from tweets, and to further classify the tweets through machine-learning (ML) techniques. The developed framework can help to understand the sentiments in our society during profound events, such as the COVID-19 pandemic. Our framework is novel in that it is a hybrid framework that combines a lexicon-based technique for tweet sentiment analysis and labeling with supervised ML techniques for tweet classification. We have evaluated the hybrid framework with a range of measures, such as precision, accuracy, recall, and F1 score. Our results indicate that the majority of the sentiments are positive (38.5%) or neutral (34.7%). Furthermore, with an accuracy of 83%, the long short-term memory (LSTM) neural network has been selected as the preferred ML technique in the framework. The evaluation results indicate that our hybrid framework has the potential to automatically classify large tweet volumes, such as the tweets on COVID-19, according to the sentiments in the society. © 2020 IEEE.

关键词： Coronavirus

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Communication-efficient and Differentially-private Distributed Nash Equilibrium Seeking with Linear Convergence

arXiv

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arXiv 2024年

作者： Chen, Xiaomeng Huo, Wei Ding, Kemi Dey, Subhrakanti Shi, Ling The Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Clear Water Bay Kowloon Hong Kong The School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen518055 China The Department of Electrical Engineering Uppsala University Uppsala751 03 Sweden

The distributed computation of a Nash equilibrium (NE) for non-cooperative games is gaining increased attention recently. Due to the nature of distributed systems, privacy and communication efficiency are two critical concerns. Traditional approaches often address these critical concerns in isolation. This work introduces a unified framework, named CDP-NES, designed to improve communication efficiency in the privacy-preserving NE seeking algorithm for distributed non-cooperative games over directed graphs. Leveraging both general compression operators and the noise adding mechanism, CDP-NES perturbs local states with Laplacian noise and applies difference compression prior to their exchange among neighbors. We prove that CDP-NES not only achieves linear convergence to a neighborhood of the NE in games with restricted monotone mappings but also guarantees ϵdifferential privacy, addressing privacy and communication efficiency simultaneously. Finally, simulations are provided to illustrate the effectiveness of the proposed method. © 2024, CC BY.

关键词： Directed graphs

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Conduction Mechanism and Influencing Factors of SiC MOSFET

Conduction Mechanism and Influencing Factors of SiC MOSFET

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2022 International Conference on Intelligent Equipment and Data Processing, IEDP 2022

作者： An, Yiping Wang, Yifan Wu, Yujin Yang, Jiazhen Electrical and Computer System Engineering Rensselaer Polytechnic Institute 1999 Burdett Avenue TroyNY12180 United States School of Microelectronics Fudan University 220 Handan Road Yangpu District Shanghai200082 China School of Advanced Technology Xi'An JIAOTONG LIVERPOOL University Renai Road 111 industrial park Jiangsu province Suzhou215000 China Nanjing University of Posts and Telecommunications 9 Wenyuan Road Qixia District Jiangsu Province Nanjing City210003 China

With the rapid development of the microelectronics industry, power semiconductor devices are getting more and more attention from the industry. At the same time, discussions on the application of power semiconductor materials have never stopped. Over the years, SiC material has stood out because of its superior physical characteristics. Compared with traditional Si MOSFET technology, SiC MOSFET technology has better physical characteristics in some aspects and has a broader market prospect. This paper introduces the conduction mechanism of SiC MOSFET and its performance factors, and the volume effect, bias temperature instability, and threshold voltage drift of SiC MOSFET are discussed. This paper is helpful in understanding the broad application prospects of SiC MOSFET technology and the difference and reform between SiC MOSFET and traditional Si MOSFET. © Published under licence by IOP Publishing Ltd.

关键词： Threshold voltage

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Trajectory Tracking for the Quadcopter UAV utilizing Fuzzy PID Control Approach

Trajectory Tracking for the Quadcopter UAV utilizing Fuzzy P...

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2020 International Conference on computer, Control, electrical, and Electronics engineering, ICCCEEE 2020

作者： Eltayeb, Ahmed Rahmat, Mohd Fuaad Eltoum, M. A Mohammed Ibrahim, M. H. Sanhoury Basri, Mohd Ariffanan Mohd Universiti Teknologi Malaysia School of Electrical Engineering Faculty of Engineering Skudai Johor1310 Malaysia Kfupm University Faculty of Computer Science and Engineering System Engineering Dept Dhahran31261 Saudi Arabia Alneelain University College of Engineering Dept. of Control Engineering Khartoum Sudan

ISBN: (纸本)9781728191119

Currently, the quadcopter Unmanned Aerial Vehicles (UAVs) are playing a significant role in combating the COVID-19 pandemic crisis, which induced the researchers to design robust control techniques. In this paper, a fuzzy PID controller is designed to stabilize and/or track the desired trajectory of the quadcopter UAV. The mathematical model of the quadcopter UAV has been briefly presented, where it has been divided into two portions, the position dynamic and the attitude dynamic subsystems. Subsequently, a robust fuzzy PID controller has been designed for both the inner loop and outer loop to control and stabilize the position and the attitude of the quadcopter, which adaptively manipulate the system's input based on the tracking error. The proposed controller is benchmarked with the conventional PID controller to show the robustness of the fuzzy PID controller. Fuzzy PID controller has been verified through simulation work utilizing Matlab/Simulink, where better performance is achieved compared with the conventional PID controller. It is found that the errors in the quadcopter's attitude and position have been significantly reduced through using fuzzy PID controller by 70% and 87%, respectively. © 2021 IEEE.

关键词： Simulink

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Feature Map Guided Strategy for Robust Multi-Resolution Image Dehazing

Feature Map Guided Strategy for Robust Multi-Resolution Imag...

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electrical Electronics and Computing Technologies (ICEECT), International Conference on

作者： Avishek Kumar Nagendra Kumar Akhilesh Kumar Mishra Gouransh Pathak Amit Chatterjee Centre for Sensors Instrumentation and Cyber-Physical System Engineering IIT Delhi New Delhi India Department of Electronics and Communication Engineering NIT Jamshedpur Jamshedpur India Department of Electrical and Electronics Engineering IILM Univeristy Greater Noida India Department of Computer Science and Engineering PIEMR-Indore Indore India School of Computing DIT University Dehradun India

ISBN: (数字)9798350378092

ISBN: (纸本)9798350378108

Presence of haze in digital images poses significant challenges in computer vision applications, adversely impacting tasks like image segmentation and object detection. To tackle this issue, this paper introduces a fusion-based dehazing strategy by integrating color conversion, adaptive gamma correction, and parameter estimation. Through multi resolution fusion of chromatic and brightness maps, the algorithm efficiently removes haze, surpassing conventional single-image approaches while maintaining computational efficacy. From comparative analysis it is evident that, the proposed strategy is highly effective in terms of improving image quality for diverse applications.

关键词： Image quality computers Image segmentation computer vision Image resolution Parameter estimation Image color analysis Digital images Object detection Computational efficiency

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Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular Balls

arXiv

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arXiv 2025年

作者： Gao, Can Tan, Xiaofeng Zhou, Jie Ding, Weiping Pedrycz, Witold The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen518060 China The National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen518060 China The School of Artificial Intelligence and Computer Science Nantong University Nantong226019 China The Faculty of Data Science City University of Macau Macau999078 China The Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland The Department of Electrical and Computer Engineering King Abdulaziz University Jeddah Saudi Arabia The Department of Computer Engineering Istinye University Istanbul Turkey

Outlier detection refers to the identification of anomalous samples that deviate significantly from the distribution of normal data and has been extensively studied and used in a variety of practical tasks. However, most unsupervised outlier detection methods are carefully designed to detect specified outliers, while real-world data may be entangled with different types of outliers. In this study, we propose a fuzzy rough sets-based multi-scale outlier detection method to identify various types of outliers. Specifically, a novel fuzzy rough sets-based method that integrates relative fuzzy granule density is first introduced to improve the capability of detecting local outliers. Then, a multi-scale view generation method based on granular-ball computing is proposed to collaboratively identify group outliers at different levels of granularity. Moreover, reliable outliers and inliers determined by the three-way decision are used to train a weighted support vector machine to further improve the performance of outlier detection. The proposed method innovatively transforms unsupervised outlier detection into a semi-supervised classification problem and for the first time explores the fuzzy rough sets-based outlier detection from the perspective of multi-scale granular balls, allowing for high adaptability to different types of outliers. Extensive experiments carried out on both artificial and UCI datasets demonstrate that the proposed outlier detection method significantly outperforms the state-of-the-art methods, improving the results by at least 8.48% in terms of the Area Under the ROC Curve (AUROC) index. The source codes are released at https://***/Xiaofeng-Tan/MGBOD. © 2025, CC BY.

关键词： Granulation

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