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检索条件"任意字段=3rd International Conference on Pattern Recognition and Machine Intelligence"

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Thermal Comparison of Dual Three-Phase PM machines with Concentrated and Distributed Windings for Integrated On-Board EV Battery Charging 3

Thermal Comparison of Dual Three-Phase PM Machines with Conc...

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3rd international conference on Sustainable Mobility Applications, Renewables and Technology, SMART 2024

作者： Ma, Xiaolu Liu, Xu Cao, Yang Zhu, Zi Qiang State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjing300130 China Electrical Machines and Drives Group University of Sheffield SheffieldS1 3JD United Kingdom

ISBN: (纸本)9798331532925

Integrated on-board EV battery charger which exploits the EV drivetrain elements into the charging process, is considered as a solution to reduce the cost of EVs. Whether in charger mode or propulsion mode, the thermal analysis in machine of the integrated system is important. In this paper, the comparative studies of dual three-phase PM machines with concentrated and distributed windings in terms of thermal performance will be carried out. The thermal performance together with electromagnetic torque, losses, and efficiency in these machines operated in charger/propulsion modes will be compared. © 2024 IEEE

关键词： machine windings

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Structural recognition of Handwritten Chinese Characters Using a Modified Part Capsule Auto-encoder 3rd

Structural Recognition of Handwritten Chinese Characters Usi...

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3rd CAAI international conference on Artificial intelligence (CICAI)

作者： Wu, Xin-Jian Ao, Xiang Zhang, Rui-Song Liu, Cheng-Lin Chinese Acad Sci State Key Lab Multimodal Artificial Intelligence Inst Automat Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9789819988495;9789819988501

Handwritten Chinese character recognition has achieved high accuracy using deep neural networks (DNNs), but the structural recognition (which offers structural interpretation, e.g., stroke and radical composition) is still a challenge. Existing DNNs treat character image as a whole and perform classification end-to-end without perception of the structure. They need a large amount of training samples to guarantee high generalization accuracy. In this paper, we propose a method for structural recognition of handwritten Chinese characters based on a modified part capsule auto-encoder (PCAE), which explicitly considers the hierarchical part-whole relationship of characters, and leverages extracted structural information for character recognition. Our PCAE is improved based on stacked capsule auto-encoder (SCAE) so as to better extract strokes and perform classification. By themodified PCAE, the character image is firstly decomposed into primitives (stroke segments), with their shape and pose information decoupled. The transformed primitives are aggregated into higherlevel parts (strokes) guided by prior knowledge extracted from writing rules. This process enhances interpretability and improves the discrimination ability of features. Experimental results on a large dataset demonstrate the effectiveness of our method in both Chinese character recognition and stroke extraction tasks.

关键词： Handwritten Chinese character recognition Structural recognition Part-whole hierarchical relationship Self-supervised learning Modified part capsule auto-encoder

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machine Learning-Based Security for Cloud Computing Challenges and Implications 3

Machine Learning-Based Security for Cloud Computing Challeng...

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3rd IEEE North Karnataka Subsection Flagship international conference, NKCon 2024

作者： Dattangire, Rahul Burle, Rushikesh Biradar, Divya Dewangan, Leelkanth Data Engineering Independent Researcher HoustonTX77002 United States Datta Meghe Institute of Higher Education and Research Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Maharashtra Wardha442001 India University of Texas at Arlington Computer Science ArlingtonTX76013 United States G H Raisoni College of Engineering Maharashtra Nagpur India

ISBN: (纸本)9798350364569

Cloud computing"is a computer model that provides end users with quantifiable, scalable, and on-demand services. These days, almost every organization uses computer technology extensively for infrastructure, cost-cutting, advanced platforms, data processing, data analytics, and other uses. End users can use any online application from anywhere to access the cloud service providers' (CSP) services. It is imperative to guarantee the security of cloud infrastructure, and a multitude of research employing various technologies is used to provide more accurate and potent defenses against cloud threats. machine learning has shown to be a more useful technology in recent years for protecting cloud infrastructures. machine learning algorithms are trained to generate models that mechanize the process of recognizing cloud threats more precisely than any other technology by using a range of actual datasets as training grounds. A couple of the most recent research that has worked on machine learning to defend against cloud attacks are examined in this paper. © 2024 IEEE.

关键词： Adversarial machine learning

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An Automated Attendance System Using Face recognition Technique based on Haar Cascade with LBPH 3

An Automated Attendance System Using Face Recognition Techni...

引用

3rd IEEE international conference on Intelligent Technologies, CONIT 2023

作者： Chauhan, Kritika Goyal, Sakshi Guru Prasad, M.S. Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350338607

In educational institutions, attendance used to be manually recorded on attendance sheets. This method doesn't work since substitute pupils can easily use them. This study introduces a face recognition-based automated attendance system that uses an image of the entire classroom to determine students' presence by matching their faces to a trained set. Everyone's faces are captured by a camera positioned in the classroom and sent to this system, which analyses them to find and recognize individual faces as well as log attendance in the main database. By capturing features on images, the Haar Cascade classifier was used to detect faces, and the Local Binary pattern Histogram (LBPH) was used to recognize faces. This system is capable of recognizing students even if they are wearing glasses or have a beard. © 2023 IEEE.

关键词： Local binary pattern

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Artificial intelligence in Cellular-V2X Networks: A Survey on Dynamic Spectrum Allocation, Traffic Prediction, and Anomaly Detection 3

Artificial Intelligence in Cellular-V2X Networks: A Survey o...

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3rd international conference for Advancement in Technology, ICONAT 2024

作者： Vishwanath, B. Reddy, P Chandrasekhar JNTUH Univ. Department of ECE College of Engg. Hyderabad India

ISBN: (纸本)9798350354171

As cellular-vehicle-to-everything (C-V2X) networks gain importance within emerging intelligent transportation systems, providing effective, secure network management is crucial. This survey paper focuses on the opportunities for applying Artificial intelligence (AI) to improve the performance of C-V2X networks. AI-assisted techniques for C-V2X network management are investigated, including Deep Q-learning techniques for dynamic spectrum allocation, machine Learning for improved traffic prediction, and anomaly detection algorithms for improving C-V2X network security. The survey reviews existing research in these domains, providing an overview of current advancements, analyzing the strengths and limitations of AI-driven solutions, and highlighting potential areas for further enhancement using AI approaches. The paper also explores methods for evaluating AI-based solutions compared to traditional non-AI methods. Additionally, it identifies key challenges and outlines future research opportunities in the application of AI to C-V2X network management. © 2024 IEEE.

关键词： Deep learning

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Self-Attention Based Image Feature Representation for Medical Visual Question Answering 3

Self-Attention Based Image Feature Representation for Medica...

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3rd IEEE international conference on Control, Instrumentation, Energy and Communication, CIEC 2024

作者： Upadhyay, Sushmita Tripathy, Sanjaya Shankar Birla Institute of Technology Mesra Department of Ece Ranchi India

ISBN: (纸本)9798350313703

Medical Visual Question Answering is an emerging field which uses artificial intelligence to effectively combine the multimodal data and generate output. Users interact with the visual data through text to generate answers, which may not always be directly accessible from the image. In this paper, a network has been proposed that aims to generate a comprehensive image feature representation, capturing the most relevant visual information present in the image, which is essential for effectively answering questions. The image features are extracted using Uniform Local Binary pattern Histogram and Gray Level Co-occurrence Matrix, which are then combined using the multihead self-attention mechanism obtaining a compact and meaningful representation. This combined representation is further fused with deep features to get the desired image feature embedding. Multimodal fusion of the image and text feature embeddings generated the final representation required for the downstream task of Visual Question Answering. © 2024 IEEE.

关键词： Local binary pattern

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Research on the Translation System of Spoken English Situational Dialogue Based on Computer Artificial intelligence 3

Research on the Translation System of Spoken English Situati...

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3rd IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2023

作者： Zhu, Yue Anhui Sanlian University Hefei China

ISBN: (纸本)9798350398410

In this paper, a sequence-to-sequence model is proposed to deal with continuous emphasis levels, and machine translation and emphasis translation are combined into a spoken English situational translation system. In the aspect of hardware design, the peripheral circuit is built with the chip as the core to form the speech recognition module. In the process of software design, relaxation endpoint method is used to improve the operation speed to realize floating point operation in the kernel. A database suitable for spoken translation system is constructed by using complex feature vectors, and remote data synchronization is realized by using SQL Server Mobile 2015 database merge replication technology. The system uses a translation engine to translate English and Chinese. There are nearly 200,000 bilingual sentences debugging in the system, which improves the language translation quality of the system. © 2023 IEEE.

关键词： Software design

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Anomalous Sound pattern Detection for machine Health Monitoring 3rd

Anomalous Sound Pattern Detection for Machine Health Monitor...

引用

3rd international conference on Artificial intelligence and Knowledge Processing (AIKP)

作者： Dalmia, Shivali Rege, Manjeet Univ St Thomas St Paul MN 55105 USA

ISBN: (纸本)9783031686160;9783031686177

Anomaly detection using audio signals from industrial machines in the manufacturing industry has gained broad interest over the last few years. For example, predictive maintenance solutions utilize raw analog signals to identify trends and patterns. In a few scenarios, an engineer working in a factory setting can tell when a machine is behaving abnormally just by hearing unexpected sounds (e.g., the loudness of sound) that are well within the human perceivable frequency range (20 Hz - lowest pitch to 20 kHz - highest pitch) which are typically concentrated in a narrow range of frequencies and amplitudes. The human perception of the amplitude of a sound is its loudness. However, the audio signal in its raw form is not always the best representation of the important features (e.g., frequencies, amplitude, peaks). Additionally, the machine learning applications which rely on using traditional digital signal processing techniques (e.g., digital signal processors, chips) have a lot of dependency on subject matter experts to tune the system for a better performance. Thus, we investigate how the digital transformation of waveform signals from microphone sensors (e.g., Audio recordings of industrial pumps, valves, slide rails) into Spectrograms (A spectrogram is a voiceprint of a signal which expresses an audio signal as an image using different colors to indicate the amplitude or strength of each frequency.) can help to monitor machine health (e.g., anomaly classification). In the pre-processing phase, raw audio signals (.WAV format) from each machine are converted to Mel Spectrogram images using short-term Fourier transformation. Then, comparative study of image classification techniques using deep convolutional neural networks (CNN) with and without data augmentation, is conducted to classify images as normal or abnormal. The approach is evaluated using Malfunctioning industrial machine investigation and inspection dataset [1-3] (MIMII dataset). Results show that the neu

关键词： Anomaly detection machine health monitoring Predictive maintenance

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HC@AIxIA 2024 - Proceedings of the 3rd AIxIA Workshop on Artificial intelligence for Healthcare, co-located with the 23rd international conference of the Italian Association for Artificial intelligence, AIxIA 2024

HC@AIxIA 2024 - Proceedings of the 3rd AIxIA Workshop on Art...

引用

3rd AIxIA Workshop on Artificial intelligence For Healthcare, HC@AIxIA 2024

The proceedings contain 27 papers. The topics discussed include: CLARITY AI: a comprehensive checklist integrating established frameworks for enhanced research quality in medical AI studies;assessing the validity of a functional status knowledge graph in a large-scale living lab;to heart via liver: a study on prognostic stratification of heart disease in MASLD patients using machine learning models;strategic optimization of blood allocation in blood banks for enhanced resource utilization;features selection through autoencoder filtering and Deepshap: an iterative algorithm;an adaptive version of the metropolis adjusted Langevin algorithm for survival prediction in a high-dimensional framework;Bayesian networks in medicine: presenting query response uncertainty for decision support;and reinforcement learning and fuzzy logic modelling for personalized dynamic treatment.

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Comparative Analysis of machine Learning Algorithms for Disease Prediction using Medical Datasets 3

Comparative Analysis of Machine Learning Algorithms for Dise...

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3rd international conference on Intelligent Data Communication Technologies and Internet of Things, IDCIoT 2025

作者： Roopa, C. Latha, R.S. Dhavamani, M. Bommisrilekkhaa, G. Angel Shalini, P. Dhanyajothi, R. Department of Computer Science and Engineering Kongu Engineering College Tamilnadu Erode India Department of Artificial Intelligence Kongu Engineering College Tamilnadu Erode India Department of Mathematics Kongu Engineering College Tamilnadu Erode India

ISBN: (纸本)9798331527549

Application of machine learning in the analysis of medical data can be said to be one of the current transformations happening within the healthcare fraternity. This paper highlights several ways in which various machine learning techniques can be used for gaining valuable insight into disparate types of medical data, such as patient records or genetic information, toward further clinical decision-making with a view to better prediction of diseases or strategies related to personalized medicine. The study critically evaluated various algorithms by considering their performances, suitability with regard to certain medical datasets they would be set upon to analyze, and to interpret the performance, particularly within the supervised learning frameworks. It talks about methodologies through which feature selection can take place to make sure that there is effective model development. Consequential applications in early disease detection as well as the optimization of their treatments are further emphasized, representing great potential for massive improvements in their recovery outcomes. The results highlight how far reaching data-driven approaches can affect the future of healthcare. It is envisioned that, with the use of such methods, the paper foresees a paradigm shift in disease diagnosis and treatment for personalized medical care and improved health outcomes. © 2025 IEEE.

关键词： Adversarial machine learning

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