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检索条件"机构=dept. of Computer Computer Systems Engineering"

共 66572 条记录，以下是541-550 订阅

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An Extended State Observer Based Super-Twisting Sliding Mode Controller for Electrostatic Actuators 50

An Extended State Observer Based Super-Twisting Sliding Mode...

引用

50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Trovato, Gianfranco Dong, Lili Mandali, Anusree Dept. Electrical and Computer Engineering Cleveland State University ClevelandOH United States Dept. Electrical and Computer Engineering Lafayette College EastonPA United States

ISBN: (纸本)9781665464543

This paper presents a super twisting sliding mode control (STSMC) system to stabilize a parallel-plate electrostatic actuator (ESA) and extend its operating range beyond the pull-in point. A voltage-controlled ESA is stable only within one-third of the full gap due to susceptibility to pull-in, nonlinearity, and sensitivity to electronic noise and disturbances. To address these challenges, an STSMC is developed to overcome the nonlinearity and instability, while an extended state observer (ESO) estimates the ESA's displacement. Implemented on a real ESA model in MATLAB/Simulink, the ESO-based STSMC increases the ESA's travel range to 99.9% of its full gap despite noise, disturbance, and parametric uncertainty. The system's stability is proved using a Lyapunov approach. © 2024 IEEE.

关键词： Sliding mode control

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Converging Diagnostic Paths: A Unified Approach to Acid-Base Imbalances in COPD Patients 2

Converging Diagnostic Paths: A Unified Approach to Acid-Base...

引用

2nd IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation, IATMSI 2024

作者： Kinikar, Atharva Shah, Manan Pune Institute of Computer Technology Dept. Information Technology Pune India Dwarkadas J Sanghvi Coe Dept. Computer Engineering Mumbai India

ISBN: (纸本)9798350360523

Chronic Obstructive Pulmonary Disease (COPD) significantly impacts patient quality of life and is a leading cause of morbidity and mortality. This paper introduces an innovative algorithm for diagnosing acid-base disorders in COPD patients, incorporating traditional and advanced methodologies like the Henderson-Hasselbalch equation and Bedside Stewart Method. The algorithm enhances the accuracy and speed of Arterial Blood Gas (ABG) analysis, crucial for COPD management. We present a comprehensive approach that includes a Gamblegram for visualizing acid-base balance and a sophisticated system architecture employing Optical Character Recognition (OCR) for efficient ABG report processing. Our findings demonstrate the algorithm's efficacy in providing rapid, detailed analysis, markedly reducing treatment initiation time. This advancement holds significant potential in revolutionizing COPD patient care by improving diagnostic accuracy and treatment strategies for acid-base disorders. © 2024 IEEE.

关键词： Gases

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An Explainable Recommender System by Integrating Graph Neural Networks and User Reviews 24

An Explainable Recommender System by Integrating Graph Neura...

引用

24th IEEE International Conference on Data Mining, ICDM 2024

作者： Batmani, Sahar Moradi, Parham Heidari, Narges Jalili, Mahdi University of Kurdistan Dept. of Computer Engineering Sanandaj Iran School of Engineering RMIT University Melbourne Australia Islamic Azad University Ilam Branch Dept. of Computer Engineering Ilam Iran

ISBN: (纸本)9798331506681

This paper introduces an explainable Graph Neural Network (GNN)-based recommender system that integrates user-item interactions and user reviews to enhance recommendation accuracy and interpretability. The proposed method leverages Temporal Convolutional Networks (TCNs) as a language model to encode user reviews into vector representations, capturing temporal dynamics and contextual information. Additionally, it extracts opinion-aspect pairs from reviews, enabling the system to understand specific product features and user sentiments. Bipartite graphs are constructed to represent interactions between users/items and opinion aspects, facilitating the integration of user reviews into the GNN framework. A contrastive learning approach is employed to combine these graphs with TCN-generated review embeddings, enhancing the system's ability to capture complex relationships. Finally, a recommendation strategy is proposed which considers relevant opinion-aspects as explanations for recommendations. The experiments conducted on several benchmarks reveal that our method outperforms its competitors. © 2024 IEEE.

关键词： Convolutional neural networks

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Cybersecurity Threat Detection Using Deep Learning and Anomaly Detection Techniques

Cybersecurity Threat Detection Using Deep Learning and Anoma...

引用

2024 International Conference on Knowledge engineering and Communication systems, ICKECS 2024

作者： Rajendran, T. Mohamed Imtiaz, N. Jagadeesh, K. Sampathkumar, Balaji Rajalakshmi Institute of Technology Department of Computer Science and Engineering Chennai India Nagarjuna College of Engineering and Technology Dept. of Information Science and Engineering Bangalore India Sri Venkateswaraa College of Technology Dept. of Computer Science and Engineering Chennai India Akshaya College of Engineering and Technology Dept. of Computer Science and Engineering Coimbatore India

ISBN: (纸本)9798350359688

Through the use of combined deep learning and anomaly detection approaches, this research investigates the area of cybersecurity threat detection. The study proves the framework's extraordinary success in recognizing as well as mitigating diverse cyber dangers, including insider threats and zero-day attacks, through practical testing and case studies. The framework establishes itself as a proactive cybersecurity solution adaptive to the changing threat landscape due to its high accuracy of 95%, low amount of false positives 5%, and quick reaction times. The recommendations cover user training, scalability, regulatory compliance, including cross-disciplinary collaboration. Behavioral analysis, AI explainability, IoT security, and countering quantum computing concerns should be the main areas of future research. These revelations help the cybersecurity industry become more resilient and well-prepared. In this research investigation, experiments on threat detection were conducted using deep learning algorithms, with a specific focus on Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs). The findings of the study reveal that a detection accuracy of 93% was achieved by CNN, while a higher accuracy of 96% in threat detection was exhibited by RNN. © 2024 IEEE.

关键词： Zero-day attack

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MLBERT: Multi-Level Fully Binarized BERT 1

MLBERT: Multi-Level Fully Binarized BERT

引用

1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Nasab, Mahdi Mohammadi Fakhire, Mahboube Salehi, Mostafa E. Modarresi, Mehdi University of Tehran College of Engineering Electrical and Computer Engineering Dept. Tehran Iran

ISBN: (纸本)9798350348637

BERT models, which use deep learning approaches for natural language comprehension, greatly improve language processing tasks in embedded devices. Their use in such devices enables sophisticated capabilities such as real-time language translation and making interactions more natural and efficient. BERT models, with their high computational complexity and large parameter count, impose significant issues in terms of processing power and memory requirements, particularly in resource-limited devices. These problems frequently require specialized hardware or optimization approaches, such as model compression, to enable efficient deployment without sacrificing performance. Binary Neural Networks, which simplify the weights and activations to binary values, are crucial for significantly reducing computational complexity and memory usage. This makes them ideal for deployment in resource-constrained environments like embedded systems and mobile devices, enabling efficient execution of complex machine learning tasks. Multi-level binarization (MLB) in neural network models takes advantage of bit-wise operations and enhances accuracy while reducing computational and memory requirements. We propose an MLB-based framework to enhance binarized BERT models which supports MLB for embedding layers as well. The results indicate that the DynaBERT model, when compressed to 1-bit precision for both Transformer layers and the entire embedding layers, along with 3-bit MLB activations, has no accuracy loss for SST-2, loses only about 0.5% of accuracy for MRPC, and 3% accuracy drop for RTE task, which is compatible with state-of-the-art. © 2024 IEEE.

关键词： Deep neural networks

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Earth Pit Monitoring System for Petrol Pumps 5

Earth Pit Monitoring System for Petrol Pumps

引用

5th International Conference on IoT Based Control Networks and Intelligent systems, ICICNIS 2024

作者： Guruprasad, Sunitha Nikitha, Amulya Rebello, Glen Noronha, Calisha Madtha, Elena St Joseph Engineering College Dept. of Computer Science & Engineering Mangaluru India

ISBN: (纸本)9798331518097

The oil industry is constantly seeking ways to monitor their facilities and equipment in a systematic way with as little human intervention as possible. Currently, monitoring of earth pits is done manually. The Hindustan Petroleum Corporation Limited (HPCL) Earth Pit Monitoring App is a mobile application that aims to enable HPCL staff to securely log in and monitor their petroleum storage sites and earthing pits. This app will make it easier to monitor all aspects of their operations, including vendors and petrol pump dealers, with timely alerts for any necessary maintenance checks. The research conducted on the modern type of earthing will be beneficial for the petroleum outlets as maintenance time and cost would be reduced due to its durability. Field research at HPCL Petroleum Outlets were conducted to observe the state of earth pits and the method of earthing used. Chemical earthing is a method of grounding electrical equipment by pouring a mixture of bentonite and graphite around a copper or iron rod driven into the ground, creating a low-resistance path for electrical charge to dissipate into the earth. It is used in areas with high soil resistance and where traditional earthing methods are not feasible. Comparison between the GI Earthing used currently, and the Chemical Earthing was drawn by comparing resistance values under different conditions. © 2024 IEEE.

关键词： Gasoline

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EfficientNet Convolutional Neural Network for Automated Cervical Spine Fracture Identification 6

EfficientNet Convolutional Neural Network for Automated Cerv...

引用

6th International Conference on Computational Intelligence and Networks, CINE 2024

作者： Sidpara, Pragati Agarwal, Anshul Swain, Debabrata Gupta, Rajeev Kumar Bhilare, Amol Upkare, Makarand Pandit Deendayal Energy University Dept. of Computer Science and Engineering Gandhinagar India Vishwakarma Institute of Technology Vishwakarma Institute of Technology Dept. of Computer Engineering Pune India

ISBN: (纸本)9798331516796

The aim of this study is to develop an automatic system that uses deep learning techniques to detect cervical spine fractures from medical images. Using a DICOM (Digital imaging and communications in medicine) images of cervical spine, the system can identify a fracture and specify which vertebrae has a fracture. This process involves first processing provided DICOM images, then applying data augmentation techniques including metadata information, finally designing neural network architecture for feature extraction. We use a method called transfer learning, utilizing a pre-trained model like EfficientNet, which is used for feature extraction, while optimizing network parameters using AdanW optimizer and CosineAnnealingWarmup learning rate scheduler. The results demonstrate the successful training of neural network models to identify fracture and that fractured vertebrae, with commendable performance and accuracy level noted during evaluation on validation data. This study has important implications for medical imaging and healthcare industries, since automated fracture detection can increase diagnostic efficiency and accuracy. Moreover, such a system would provide good support to healthcare professionals, specifically radiologists, to identify cervical spine fracture easily. © 2024 IEEE.

关键词： Digital Imaging and Communications in Medicine (DICOM)

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INTELLIGENT MANAGEMENT OF INTEGRATED ENERGY systems IN REMOTE MARITIME ENVIRONMENTS UTILIZING FUZZY MODES 14

INTELLIGENT MANAGEMENT OF INTEGRATED ENERGY SYSTEMS IN REMOT...

引用

14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Alamaniotis, Miltiadis Fevgas, Athanasios Dept. of Electrical and Computer Engineering University of Texas at San Antonio San AntonioTX United States Dept. of Electrical and Computer Engineering University of Thessaly Volos Greece

ISBN: (纸本)9781837242689

This paper explores isolated maritime environments equipped with an integrated energy system (IET) to fulfil the energy requirements of nearby communities. These environments are presumed to lack grid access but possess a connection point for a floating energy source capable of supplying consistent electricity for a limited duration. The study introduces a novel management system for the efficient operation of IES in such environments, utilizing an innovative multi-mode approach. Specifically, the system includes several management modes, each employing a distinct management strategy for the IET. These modes are implemented using fuzzy logic tools, with a core of IF/THEN control rules that enable flexible and rapid decision-making. The fundamental concept is that the use of modes in decision-making inherently prioritizes specific operational objectives and consumption needs based on current conditions. The proposed system comprises three modes and is tested through a series of simulated scenarios. Results indicate that the IET can provide flexible energy management that adapts to dynamic conditions, outperforming a single-strategy system. © The Institution of engineering & Technology 2024.

关键词： Fuzzy logic

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LegalAI Across Languages: A Comprehensive Survey on Tasks, Methodologies and Challenges 1

LegalAI Across Languages: A Comprehensive Survey on Tasks, M...

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1st International Conference on AIML-Applications for engineering and Technology, ICAET 2025

作者： Deshpande, Anjali Deshmukh, Pratiksha COEP Technological University Dept. of Computer Science and Engineering Pune India PICT Dept. of AI & DS Pune India School of Computational Sciences COEP Technological University Dept. of Computer Science and Engineering Pune India

ISBN: (纸本)9798350355611

Natural Language Processing (NLP) is revolutionizing the legal domain, enabling tasks such as predicting legal outcomes, summarizing complex documents, identifying key entities, and assessing bail risks. This survey paper presents a comprehensive review of the existing research on NLP tasks applied to legal documents and judgments across various languages, with a special emphasis on low-resource languages. Despite the extensive work done in widely spoken languages like English, there remains a critical need to extend these advancements to languages with limited resources and data availability. This paper categorizes and analyzes the NLP tasks in the legal domain based on the languages they target. It highlights the methodologies employed, datasets utilized, and the performance of different models across diverse languages. Furthermore, it identifies the gaps in current research and proposes potential directions for future work, particularly in improving the accessibility and applicability of NLP solutions for legal texts in underrepresented languages. By providing a comprehensive overview of advancements and obstacles, this survey seeks to assist future research endeavors that bridge the disparity between high-resource and low-resource languages in legalAI applications. © 2025 IEEE.

关键词： Risk assessment

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Framework for Cloud-Based Data Management in Autonomous Vehicles

Framework for Cloud-Based Data Management in Autonomous Vehi...

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2025 IEEE International Students' Conference on Electrical, Electronics and computer Science, SCEECS 2025

作者： Pallapothu, Harshith Gopi Krishna, G. Sunil, Digvijay Teja Yadav, S. Nagasundari, S. Pes University Dept. of Computer Science & Engineering Bengaluru India Research Center for Information Security Forensics and Cyber Resilience Pes University Dept. of Computer Science & Engineering Bengaluru India

ISBN: (纸本)9798331529833

The increasing adoption of autonomous vehicles has driven the need for robust data management solutions that support real-time operations and ensure vehicle safety and efficiency. This work introduces a cloud-based framework for management of sensor data from autonomous vehicles, focusing on optimizing payload transmission rates. The framework leverages AWS IoT Core and AWS IoT Analytics to ensure efficient data flow from vehicle sensors to cloud storage. This framework demonstrates its potential for scalable deployment in real-world autonomous vehicle networks, contributing to the evolution of connected vehicle technologies and intelligent transport systems. This framework ensures reliable data transmission up to 250 payloads per second, beyond which data loss occurs. © 2025 IEEE.

关键词： Cloud platforms

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