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3rd information technology to Enhance e-Learning and Other Application, IT-ELA 2022

作者： Sahib, Abdullah Al-Baidhani, Humam AL-Furat AL-Awsat Technical University Department of Electronic and Communication Technologies Najaf Iraq Imam Ja'afar Al-Sadiq University Faculty of Information Technology Department of Computer Techniques Engineering Baghdad Iraq

ISBN: (纸本)9798350334968

Power factor (PF) is one of the important aspects affecting the performance of the electrical network. This phenomenon results from an increase in inductive loads, which leads to lower voltage, increase losses, and lower efficiency in the electrical network. Different types of shunt capacitor bank (SCB) configurations are installed in the distribution substation (DS), which can be grounded or ungrounded, fused or without fuses. These SCBs provide voltage support, power factor correction, power transport increment, loss reduction, and enhancement of system quality. This paper presents the evaluation of two fixed SCBs isolated by isolator at 33 Kv busbar in DS for PFC. The work is simulated and analyzed using MATLAB. © 2022 IEEE.

关键词： Busbars

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PrivBox: Privacy-Preserving Deep Packet Inspection with Dual Double-masking Obfuscated Rule Generation

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IEEE Transactions on Dependable and Secure Computing 2025年

作者： Wu, Pengfei Ning, Jianting Huang, Xinyi Chen, Rongmao Zhang, Kai Liang, Kaitai Wuhan University School of Cyber Science and Engineering Wuhan China Singapore Management University School of Computing and Information Systems Singapore City University of Macau Faculty of Data Science Macau China Fujian Normal University College of Computer and Cyber Security Fuzhou China Jinan University College of Cyber Security Guangzhou China National University of Defense Technology College of Computer Changsha China Shanghai University of Electric Power College of Computer Science and Technology Shanghai China Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science Netherlands

Many network middleboxes have been deployed to perform deep packet inspection (DPI) over packet payloads. However, such middleboxes cannot accomplish their tasks when the traffic is encrypted. BlindBox (SIGCOMM 2015) provided the first solution for performing DPI over encrypted traffic. To improve its efficiency, a later proposal PrivDPI (CCS 2019) introduced a practical technique to generate encrypted rules. However, a recent proposal P2DPI (ASIACCS 2021) showed that the rule generator in PrivDPI can comprise the user's privacy. In this paper, we present a new attack on P2DPI and show that the privacy of its endpoints can still be compromised by the rule generator. We comprehensively analyze the vulnerability of prior studies and present PrivBox, a new DPI system that achieves the same privacy guarantee as BlindBox while maintaining practical efficiency. This is based on a new technique called dual double-masking obfuscated rule generation. For a ruleset of 3,000, PrivBox achieves connection establishment time on the endpoint side comparable to PrivDPI and supports up to 4,672 token encryptions per second, which is sufficient for a number of real-world applications. Overall, our experiment demonstrates that PrivBox is practical and well-suited for short, frequently established sessions, especially when token repeating is common. ©2004-2012 IEEE.

关键词： Differential privacy

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A non-interactive hybrid method for structured offline documents.: Application on the mathematical expression 8

A non-interactive hybrid method for structured offline docum...

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8th International Conference on information technology Trends, ITT 2022

作者： Ghorbel, Achraf Rejeb, Oussema Jabra, Marwa Ben Higher Colleges of Technology Computer Information Science Department Sharjah United Arab Emirates University of Sharjah Sustainable Energy Development Research Group United Arab Emirates Faculty of Engineering Uni de Moncton Canada

ISBN: (数字)9781665487429

ISBN: (纸本)9781665487412

This article describes a novel method for interpreting sketches. This method is based on a breadth-first competitive analysis of the analysis tree. In contrast to well-known structural approaches, this method enables the simultaneous evaluation of multiple possible recognition hypotheses within the dynamic local context of a document. The decision process selects the best hypotheses at each stage of the analysis. This article demonstrates this novel approach to mathematical expression recognition. © 2022 IEEE.

关键词： Text analysis Market research information technology

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ChatGPT: Applications, Opportunities, and Threats

arXiv

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arXiv 2023年

作者： Bahrini, Aram Khamoshifar, Mohammadsadra Abbasimehr, Hossein Riggs, Robert J. Esmaeili, Maryam Majdabadkohne, Rastin Mastali Pasehvar, Morteza Department of Systems and Information Engineering University of Virginia CharlottesvilleVA United States School of Computer Engineering Iran University of Science and Technology Tehran Iran Faculty of Information Technology and Computer Engineering Azarbaijan Shahid Madani University Tabriz Iran Department of Industrial Engineering Faculty of Engineering Alzahra University Tehran Iran Department of Medicine Faculty of Medical Sciences The American University Managua Nicaragua Department of Industrial Engineering Faculty of Engineering Islamic Azad University Parand Branch Tehran Iran

Developed by OpenAI, ChatGPT (Conditional Generative Pre-trained Transformer) is an artificial intelligence technology that is fine-tuned using supervised machine learning and reinforcement learning techniques, allowing a computer to generate natural language conversation fully autonomously. ChatGPT is built on the transformer architecture and trained on millions of conversations from various sources. The system combines the power of pre-trained deep learning models with a programmability layer to provide a strong base for generating natural language conversations. In this study, after reviewing the existing literature, we examine the applications, opportunities, and threats of ChatGPT in 10 main domains, providing detailed examples for the business and industry as well as education. We also conducted an experimental study, checking the effectiveness and comparing the performances of GPT-3.5 and GPT-4, and found that the latter performs significantly better. Despite its exceptional ability to generate natural-sounding responses, the authors believe that ChatGPT does not possess the same level of understanding, empathy, and creativity as a human and cannot fully replace them in most situations. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Asymmetric Modulated Predictive Current Control for Dual Three-Phase PMSM With Improved Performance

Asymmetric Modulated Predictive Current Control for Dual Thr...

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International Conference on Power Electronics and Motion Control (IPEMC)

作者： Ze Li Xingyu Zheng Jinhui Xia Hangan Liu Xiaonan Gao Jose Rodriguez Liling Wang Zhaoyan Zhang Zhiheng Liu College of Electronic and Information Engineering Hebei University China School of Automation Southeast University China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Sweden Faculty of Engineering Universidad San Sebastian Chile

ISBN: (数字)9798350351330

ISBN: (纸本)9798350351347

Dual three-phase PMSM (DTP-PMSM) has gained significant attention benefiting from their exceptional reliability and high torque density. However, DTP-PMSM exhibits poor steady-state performance and large calculation amount with the conventional finite-control-set predictive current control. To address these concerns, an asymmetric modulated predictive current control (AM-PCC) for DTP-PMSM is presented. Therein, the three-phase decomposition and deadbeat control scheme are combined to eliminate the weighting factor and optimize the execution efficiency. An asymmetric modulation strategy is presented and integrated into PCC to reduce the current harmonics and switching frequency. Extensive simulation tests are conducted to demonstrate the effectiveness of the proposed AM-PCC scheme.

关键词： Current control Torque Switching frequency Dynamics Modulation Harmonic analysis Power electronics

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Enhancing Security and Scalability of Metaverse with Blockchain-based Consensus Mechanisms 15

Enhancing Security and Scalability of Metaverse with Blockch...

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15th International Conference on Electronics, computers and Artificial Intelligence, ECAI 2023

作者： Rajawat, Anand Singh Goyal, S.B. Goyal, Aarti Rajawat, Kavita Raboaca, Maria Simona Verma, Chaman Mihaltan, Traian Candin School of Computer Science & Engineering Sandip University Nashik India City University Faculty of Information Technology Petaling Jaya Malaysia Mind Intellect Technology Indore India National Research and Development Institute for Cryogenics and Isotopic Technologies Icsi Energy Department Ramnicu Valcea Romania Eötvös Loránd University Faculty of Informatics Department of Media and Educational Informatics Budapest Hungary Technical University of Cluj-Napoca Faculty of Building Services Cluj-Napoca Romania

ISBN: (纸本)9798350321388

This study examines how blockchain technology and consensus mechanisms can safeguard and grow the metaverse ecosystem. Blockchain is transparent and decentralized. It is unchangeable, cryptographically verified, and runs without a central authority. Blockchain technology can secure the metaverse and users' data. This study compares blockchain-based consensus algorithms such as POW(PoW), PoS, and PBFT to ensure metaverse safety. These consensus mechanisms provide safe, decentralized transaction verification, waste reduction, and network consensus. Studying blockchain technology's metaverse scaling restrictions and scaling solutions, including sharding, sidechains, and off-chain protocols. These methods increase throughput, parallel computing, and transaction speed. They improve the metaverse's eco-system. Quantitative analysis, simulations, and experiments determine the security and scalability of blockchain-based metaverse consensus processes. Changes will be quantified by measuring transaction speed, network throughput, and resource consumption. This study enables safe, scalable metaverse environments. Blockchain-based consensus mechanisms allow the metaverse to retain user data safely, halt undesirable conduct, and allow users to join without trusting each other. Scalability solutions declutter networks, enabling the metaverse to handle more individuals and transactions. Blockchain-based consensus mechanisms could make the metaverse more secure, private, and scalable. Users will trust each other, create new apps, and be more open to this groundbreaking digital world. © 2023 IEEE.

关键词： Scalability

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IALS: Innovative Approach for Lung Segmentation Applying Artificial Intelligence and Deep Learning

IALS: Innovative Approach for Lung Segmentation Applying Art...

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International Conference on computer, Electrical & Communication engineering (ICCECE)

作者： Mohammad Alamgir Hossain Fazal Imam Shahi Sadia Husain Abdelrazig Suliman Mohammad Shahid Kamal Yasir Ahmad Mohammad Mazedul Huq Talukdar R. John Martin Department of computer Science College of Engineering & Computer Science Jazan University Jazan Saudi Arabia eLearning Center Jazan University Jazan Saudi Arabia Department of Information Technology Faculty of Computers Information Technology University of Bisha Saudi Arabia Department of Foreign Languages College of Arts and Humanities Jazan University Jazan Saudi Arabia

ISBN: (数字)9798331513894

ISBN: (纸本)9798331513900

The automated segmentation of lungs within chest CT scans remains crucial for many medical applications that require disease diagnosis and treatment design. The presented deep incremental learning (DIL) system addresses fundamental limitations that arise during lung segmentation processes. The exceptional segmentation outcomes result from the neural network structure which includes masked RCNN+UNet for tissue segmentation and GAN+VARMA for classification. Medical literature identifies lung tissue structures as difficult to study because they present irregular forms and complex structures. The task of obtaining precise segmentation proves very challenging. The application of masked RCNN+UNet enables shape control and improves segmentation precision of the lung system. The DIL function helps maintain improved features within segmented images. The applied framework enables improved performance through DSC and MAE and JI metrics evaluation. Experimental evidence shows that the new framework achieves superior performance compared to current top-ranking methods for lung segmentation. The proposed framework exhibits excellent precision and detection efficiency with a DSC value of 0.96 and JI value of 0.93 and a low MAE $\lt 0.01$. The new extraction method could advance medical image examination systems within healthcare settings.

关键词： Deep learning Image segmentation Shape control Lungs Pulmonary diseases Computed tomography Predictive models Real-time systems Medical diagnostic imaging Electronics packaging

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FolkRank++: An optimization of folkrank tag recommendation algorithm integrating user and item information

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KSII Transactions on Internet and information Systems 2021年第1期15卷 1-19页

作者： Zhao, Jianli Zhang, Qinzhi Sun, Qiuxia Huo, Huan Xiao, Yu Gong, Maoguo College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China College of Mathematics and Systems Science Shandong University of Science and Technology Qingdao China Faculty of Engineering and Information Technology University of Technology SydneyAustralian Australia Key Laboratory of Intelligent Perception and Image Understanding Ministry of Education Xidian University Xi'an China

The graph-based tag recommendation algorithm FolkRank can effectively utilize the relationships between three entities, namely users, items and tags, and achieve better tag recommendation performance. However, FolkRank does not consider the internal relationships of user-user, item-item and tag-tag. This leads to the failure of FolkRank to effectively map the tagging behavior which contains user neighbors and item neighbors to a tripartite graph. For item-item relationships, we can dig out items that are very similar to the target item, even though the target item may not have a strong connection to these similar items in the user-item-tag graph of FolkRank. Hence this paper proposes an improved FolkRank algorithm named FolkRank++, which fully considers the user-user and item-item internal relationships in tag recommendation by adding the correlation information between users or items. Based on the traditional FolkRank algorithm, an initial weight is also given to target user and target item's neighbors to supply the user-user and item-item relationships. The above work is mainly completed from two aspects: (1) Finding items similar to target item according to the attribute information, and obtaining similar users of the target user according to the history behavior of the user tagging items. (2) Calculating the weighted degree of items and users to evaluate their importance, then assigning initial weights to similar items and users. Experimental results show that this method has better recommendation performance. © 2021 Korean Society for Internet information. All rights reserved.

关键词： Behavioral research

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Frequency-aware Interaction Network for Ultrasound Image Segmentation

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IEEE Transactions on Circuits and Systems for Video technology 2024年

作者： Wang, Dongfang Zhou, Tao Zhang, Yizhe Gao, Shangbing Yang, Jian Nanjing University Of Science And Technology Pca Lab Key Lab Of Intelligent Percept. And Syst. For High-Dimensional Information Of Ministry Of Education Jiangsu Key Lab Of Image And Video Understanding For Social Security School Of Computer Science And Engineering Nanjing210094 China Huaiyin Institute Of Technology Faculty Of Computer And Software Engineering Huaian223003 China

Accurate segmentation of medical ultrasound images is crucial for guiding treatment decisions and assessing intervention effectiveness. The challenge of segmenting lesions in ultrasound images arises from factors such as low contrast, high speckle noise, artifacts, and blurred boundaries. Furthermore, this complexity varies significantly among lesions in different cases. While methods based on Convolutional Neural Networks (CNNs) and Transformers have shown promising results in this field, each approach possesses distinct advantages and limitations. To address these challenges, we propose a novel Frequency-aware Interaction Network (FINet). At the core of our FINet lies the proposed Multi-scale Frequency-aware Self-attention (MFS) module, which effectively captures multi-scale feature information within the self-attention layer. This enables our network to model both local and global features, capitalizing on the strengths of both CNNs and Transformers. Additionally, a frequency-aware network is introduced to learn the interactions between spatial locations in the frequency domain to enhance detailed feature representation such as edges. Furthermore, we present a collaborative interactive decoder network, in which a Selective Feature Interaction (SFI) module is proposed to facilitate the semantic and boundary feature interaction, resulting in more precise segmentation outcomes. Experimental results on four medical ultrasound image datasets show the superiority of our FINet over other state-of-the-art segmentation methods. More importantly, our model achieves an excellent trade-off between performance and computational efficiency. © 1991-2012 IEEE.

关键词： Ultrasonic imaging

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CFIS-YOLO: A Lightweight Multi-Scale Fusion Network for Edge-Deployable Wood Defect Detection

arXiv

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arXiv 2025年

作者： Kang, Jincheng Cen, Yi Cen, Yigang Wang, Ke Liu, Yuhan School of Information and Engineering Minzu University of China Beijing100081 China School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China Faculty of Science The University of Hong Kong 999077 Hong Kong

Wood defect detection is critical for ensuring quality control in the wood processing industry. However, current industrial applications face two major challenges: traditional methods are costly, subjective, and labor-intensive, while mainstream deep learning models often struggle to balance detection accuracy and computational efficiency for edge deployment. To address these issues, this study proposes CFIS-YOLO, a lightweight object detection model optimized for edge devices. The model introduces an enhanced C2f structure, a dynamic feature recombination module, and a novel loss function that incorporates auxiliary bounding boxes and angular constraints. These innovations improve multi-scale feature fusion and small object localization while significantly reducing computational overhead. Evaluated on a public wood defect dataset, CFIS-YOLO achieves a mean Average Precision (mAP@0.5) of 77.5%, outperforming the baseline YOLOv10s by 4 percentage points. On SOPHON BM1684X edge devices, CFIS-YOLO delivers 135 FPS, reduces power consumption to 17.3% of the original implementation, and incurs only a 0.5 percentage point drop in mAP. These results demonstrate that CFIS-YOLO is a practical and effective solution for real-world wood defect detection in resource-constrained environments. Copyright © 2025, The Authors. All rights reserved.

关键词： Deep learning

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