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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Magnetics Design Optimization for LLC Converter employing Machine learning 11

Magnetics Design Optimization for LLC Converter employing Ma...

引用

11th international conference on Power Electronics - ECCE Asia, ICPE 2023-ECCE Asia

作者： Tung, Nguyen Tan Pong, Man-Hay Chiu, Huang-Jen National Taiwan University of Science and Technology Department of Electronics and Computer Engineering Taiwan

ISBN: (纸本)9788957083505

this paper proposes an intelligent and machine-learning based optimization method that targets to optimal windings layer setup for LLC converter transformer with small number of optimizing iterations. the research utilizes Bayesian optimization (BO) to guide designers toward winding layers setup that achieves the maximum efficiency at nominal operating point of LLC converters. Furthermore, a new multiphysics simulation platform is proposed for feeding efficiencies to optimizing iterations. this simulation platform is able to provide entire losses estimation, high simulation accuracy is verified by measurement. An LLC converter prototype with 33V input and 400V/250W output is implemented to demonstrate effectiveness of proposed optimization method. © 2023 the Korean Institute of Power Electronics.

关键词： Simulation platform

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Dual-Module Feature Alignment Domain Adversarial Model for Entity Resolution 11

Dual-Module Feature Alignment Domain Adversarial Model for E...

引用

11th IEEE international conference on Behavioural and Social Computing, BESC 2024

作者： Song, Hongtao Liu, Miao Zhang, Shuang Han, Qilong Harbin Engineering University Harbin China

ISBN: (纸本)9798331531904

Entity Resolution (ER) is a fundamental task in data integration, aiming to identify data objects across different sources that refer to the same real-world entity. In recent years, deep learning solutions have become the mainstream approach. However, their effectiveness often depends on the availability of large amounts of labeled training data, which is often challenging to obtain in many scenarios. To address this challenge, we propose utilizing Domain Adversarial Neural Networks (DANN) for efficient domain adaptation, transferring knowledge from labeled source ER datasets to new target ER datasets. However, domain adaptation for ER faces unique challenges: (1) it requires transferring knowledge or parameters from individual records, not just considering the similarity of record pairs;(2) there is significant class imbalance between positive and negative samples, affecting the model's generalization ability and accuracy;(3) in DANN, adjusting the classifier solely using labeled data from the source domain leads to features biased towards the source domain. To address these challenges, we propose a model called the Dual-Module Feature Alignment Domain Adversarial Entity Resolution (DFA-ER). DFA-ER combines a feature extractor based on relationship reconstruction and a dual-classifier domain adversarial network optimized with the Maximize Classifier Difference (MCD) strategy, to enhance applicability to ER tasks. DFA-ER adopts a dual-module network architecture and uses adversarial training to align the feature distribution of entity record pairs in different domains from the perspectives of domain invariant features and domain discriminant features, thereby improving the performance of target domain tasks. Experimental results show that, compared to baseline methods, DFA-ER achieves better performance across various dataset settings. © 2024 IEEE.

关键词： Adversarial machine learning

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Review on Research of automated Machine learning 7

Review on Research of Automated Machine Learning

引用

7th international conference on Computer Science and Artificial Intelligence, CSAI 2023

作者： Zhong, Yuyanzhen Yang, Chuan Su, Xinyi Li, Biao Huang, Xiaoping Shuai, Yong Chongqing Ceprei Industrial Technology Research Institute Co. Ltd. China

ISBN: (纸本)9798400708688

automated Machine learning (AutoML) can automatically discover high-performance models to build deep learning systems without human assistance, with the ultimate goal of reducing the complexity and entry barriers of building deep learning systems. Although AutoML has achieved a certain degree of automation through four important steps: data preparation, feature engineering, model generation, and model evaluation, there is still a significant gap compared to the ultimate ideal of achieving truly intelligent lifelong learning. therefore, a deep understanding of AutoML can help drive the development of artificial intelligence. Firstly, we comprehensively reviewed the latest technologies and achievements involved in these four steps, then we introduced their shortcomings and challenges. Secnodly, a detailed introduction was given to the existing AutoML libraries and the theoretical and practical applications of AutoML. Finally, we summarized AutoML models and Proposed an outlook. © 2023 ACM.

关键词： learning systems

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NEARLY MINIMAX OPTIMAL OFFLINE REINFORCEMENT learning WIth LINEAR FUNCTION APPROXIMATION: SINGLE-AGENT MDP AND MARKOV GAME 11

NEARLY MINIMAX OPTIMAL OFFLINE REINFORCEMENT LEARNING WITH L...

引用

11th international conference on learning Representations, ICLR 2023

作者： Xiong, Wei Zhong, Han Shi, Chengshuai Shen, Cong Wang, Liwei Zhang, Tong Department of Mathematics The Hong Kong University of Science and Technology Hong Kong Center for Data Science Peking University China Department of Electrical and Computer Engineering University of Virginia United States National Key Laboratory of General Artificial Intelligence Peking University China School of Intelligence Science and Technology Peking University China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong

Offline reinforcement learning (RL) aims at learning an optimal strategy using a pre-collected dataset without further interactions with the environment. While various algorithms have been proposed for offline RL in the previous literature, the minimax optimality has only been (nearly) established for tabular Markov decision processes (MDPs). In this paper, we focus on offline RL with linear function approximation and propose a new pessimism-based algorithm for offline linear MDP. At the core of our algorithm is the uncertainty decomposition via a reference function, which is new in the literature of offline RL under linear function approximation. theoretical analysis demonstrates that our algorithm can match the performance lower bound up to logarithmic factors. We also extend our techniques to the two-player zero-sum Markov games (MGs), and establish a new performance lower bound for MGs, which tightens the existing result, and verifies the nearly minimax optimality of the proposed algorithm. To the best of our knowledge, these are the first computationally efficient and nearly minimax optimal algorithms for offline single-agent MDPs and MGs with linear function approximation. © 2023 11th international conference on learning Representations, ICLR 2023. All rights reserved.

关键词： Reinforcement learning

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IWP-PIDL: Interpretable-Weight-Parameters Physics-Informed Deep learning Model for Traffic State Estimation Based on Vehicle Trajectory 27

IWP-PIDL: Interpretable-Weight-Parameters Physics-Informed D...

引用

27th IEEE international conference on intelligent Transportation Systems, ITSC 2024

作者： Li, Zhihao Li, Ye Gruyer, Dominique Zargayouna, Mahdi Tu, Meiting College of Transportation Engineering Tongji University Key Laboratory of Road and Traffic Engineering Ministry of Education Shanghai201804 China Université Gustave Eiffel Champs sur Marne77447 France

ISBN: (纸本)9798331505929

Traffic state estimation is crucial to improve the safety and efficiency of Connected-automated Vehicle. In this task, physical information deep learning has been widely used by researchers due to its excellent performance. Existing studies have neglected the traffic shock waves generated due to inefficient road operation and the interpretation of weight parameters. therefore, this paper proposes a framework considering various penetration rates that integrates physic information on traffic shock waves and establishes interpretable weighting parameter through the regional training priority metrics. Subsequently, the study employs next generation simulation dataset to randomly select trajectory data based on a specified ratio and evaluates the proposed model. the experiment shows that the proposed models all have more than 10% improvement in accuracy compared to the PIDL model with LWR and DL model. the design of weighting parameters proves that deep learning helps fast convergence in the early stage and physical information helps deep convergence in the later stage. the proposed model also provides the potential to identify 'moving bottlenecks,' which is a crucial link for enhancing the utilization of road resources. © 2024 IEEE.

关键词： Shock waves

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Implementation and Evaluation of a Biometric Face Recognition System for Authentication in Digital learning 11

Implementation and Evaluation of a Biometric Face Recognitio...

引用

11th international conference on ICT for Smart Society, ICISS 2024

作者： Faiza, Delsina Farell, Geovanne Delianti, Vera Irma thamrin Anori, Sartika Wahyudi, Rido Universitas Negeri Padang Electronic Engineering Sumatera Barat Padang City Indonesia

ISBN: (纸本)9798350389654

In the world of modern education, e-learning technologies are becoming more popular and so as digital security has become an important part. It is also identified as a transitional period where e-learning technology usage is steadily growing. Traditional authentication systems have always been susceptible to data breaches and misuse, hence not dependable. the current study implementation and evaluate facial recognition system in the digital learning authentication process at SMK N 2 Padang. the primary objective includes optimizing security and operational efficiency within the school environment. this paper uses the 4D feedback model (Define, Design, Develop and Disseminate) to design, develop and assess this system. the metrics assessed include detection accuracy, speed and usability efficiency security and integration with digital platforms. Evaluation results show that all four systems (System A, B, C and D) pass the validity criteria with final scores of more than 0.6, demonstrating that the systems are effective in a digital learning environment. System A achieved the highest score of 0.882, indicating superior performance in terms of recognition accuracy, security, and integration with digital platforms. Systems C and D also performed well, with scores of 0.861 and 0.849, respectively, while System B met the established performance criteria despite having a slightly lower score of 0.791. the use of facial recognition technology not only enhances digital access security but also supports administrative efficiency and protects student privacy. this has potential practical implications for the development of digital education authentication technology and generalizes to other educational contexts that demand facial recognition biometric authentication. © 2024 IEEE.

关键词： Contrastive learning

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Deep learning Models to Predict Hip Joint Kinetics From Kinematics During Gait 24

Deep Learning Models to Predict Hip Joint Kinetics From Kine...

引用

11th international conference on Biomedical and Bioinformatics engineering, ICBBE 2024

作者： Hulleck, Abdul Aziz Alshehhi, Aamna Abdullah, Muhammad Katmah, Rateb Khalaf, Kinda El-Rich, Marwan Khalifa University Abu Dhabi United Arab Emirates

ISBN: (纸本)9798400718274

Hip joint reaction forces serve as crucial indicators of underlying hip disorders such as osteoarthritis, avascular necrosis, and femoro-acetabular impingements. Quantifying hip kinetics traditionally relies on telemeterized implants or non-invasive conventional gait labs, which measure kinematics and external kinetics in controlled laboratory settings. these methods, although non-invasive, require specialized expertise, particularly in the workflows of musculoskeletal modeling. this study aims to develop a sequence-to-sequence (seq2seq) model capable of predicting hip joint reaction forces in three planes using positional data obtained from either marker-based or marker-less motion capture systems. the model was trained and validated using a publicly available database of eighty healthy subjects, employing leave-one-subject-out cross-validation. through optimization of hyperparameters and early stopping techniques, the model achieved RMSE values below 0.5% BW for antero-posterior shear force, 1% BW for compressive forces, and 0.31% BW for medio-lateral shear forces. Normalized RMSE values were less than 0.25% across all components of hip joint reaction forces. In conclusion, this study proposes a deep learning seq2seq model as a viable method for accurately predicting hip joint reaction forces in all three planes. these seq2seq models present a cost-effective alternative to traditional and cumbersome gait lab equipment and current musculoskeletal modeling approaches used in hip kinetics assessment. Copyright © 2024 held by the owner/author(s). Publication rights licensed to ACM.

关键词： Shear flow

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Exploring Gender Dynamics in Motorcycle Traffic Violations through Gender Detection Technology 6

Exploring Gender Dynamics in Motorcycle Traffic Violations T...

引用

6th international conference on intelligent Computing in data Sciences, ICDS 2024

作者： Charef, Ayoub Jarir, Zahi Quafafou, Mohamed EMSI Moroccan School of Engineering Sciences LAMIGEP Research Laboratory Marrakech Morocco Marrakech Morocco University of Aix-Marseille Department of Computer Science Marseille France

ISBN: (纸本)9798350351200

Urban traffic management faces significant challenges due to non-compliance with traffic rules, particularly among motorcycle riders. this study introduces an innovative approach employing the YOLOv9 object detection framework to monitor and analyze motorcycle violations at busy intersections in Marrakech. By leveraging advanced machine learning techniques, the study aimed to detect helmet usage, traffic light violations, and assess gender-based differences in violation rates. data were collected using smartphone cameras at strategic locations during peak traffic times across selected days. the captured video footage was analyzed using the YOLOv9 model, which was pretrained on diverse traffic scenes to enhance its accuracy and reliability in real-Time object detection. the findings reveal that the automated system not only aligns closely with manual counting but also offers greater consistency and efficiency. Key results indicated a pronounced gender disparity in violation frequencies and provided insights into the temporal patterns of traffic rule infractions. Moreover, the study highlighted minor discrepancies due to environmental factors, which were systematically addressed to refine the detection process. Contextual factors in interpreting detection results. © 2024 IEEE.

关键词： Motorcycles

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Crunching data for Early Warning: Predicting Renal Conditions in Real Time 11

Crunching Data for Early Warning: Predicting Renal Condition...

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11th international conference on Reliability, Infocom Technologies and Optimization, ICRITO 2024

作者： Jaipurkar, Manasvi Ragit, Neha Tambuskar, Chetana Agrawal, Rahul G H Raisoni College of Engineering Nagpur India

ISBN: (纸本)9798350350357

Chronic Renal Disease (CRD) poses a significant global health concern, impacting approximately one in five men and one in four women. Its insidious progression and late-stage diagnosis present a formidable challenge within the medical community. In response, our research has been dedicated to the development of a real-time CRD identification system, leveraging a dataset encompassing 798 individuals from a hospital. this dataset laid the groundwork for crafting an exceptionally accurate machine learning model. seamlessly integrated into a user-friendly web application using Streamlittechnology. Among the array of machine learning algorithms we explored, the LightGBM(light gradient-boosting machine)Algorithm distinguished itself by achieving an impressive 98.12% accuracy in predicting CRD. Beyond this accuracy, our investigation delved into the critical attributes that bear the most significance in CRD detection gives the top attributes from each algorithms with providing invaluable insights for healthcare professionals. Our interactive web application not only furnishes accurate predictions but also empowers healthcare providers and individuals to proactively assess CRD and institute early interventions. Our ultimate goal is to make substantial strides in addressing this challenging medical condition, thereby contributing to heightened survival rates and improved overall outcomes for individuals grappling with CRD on a global scale. © 2024 IEEE.

关键词： Diagnosis

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Multi-Task learning Network Optimization Based on Weight Adaptive 11

Multi-Task Learning Network Optimization Based on Weight Ada...

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11th IEEE international conference on Behavioural and Social Computing, BESC 2024

作者： Cao, Zizhuo Li, Yidong Ding, Chuntao Zhang, Zhuxi School of Computer Science and Technology Beijing Jiaotong University Key Laboratory of Big Data & Artificial Intelligence in Transportation Ministry of Education Beijing100044 China General Logistics Information Center Beijing100036 China

ISBN: (纸本)9798331531904

Multi-task learning has emerged as a significant topic in artificial intelligence research, where a singular network model performs numerous tasks. this approach simultaneously processes multiple related tasks and shared knowledge, enhancing model generalization while increasing efficiency. this methodology provides innovative solutions to complex real-world problems. However, the single-model-based approach for multi-task learning suffers from inter-task interference in practice. therefore, the exploration of more efficient multi-task learning strategies, aimed at balancing the synergy and conflict among tasks and minimizing the dependence on computational resources, is pivotal for the field's future progress. We propose a strategy that autonomously adjusts both the parameters and structures of the model to alleviate gradient interference in multi-task learning. this method includes integrating a lightweight, weight-adaptive module that enhances the network's ability to process tasks by optimally balancing parameter sharing and isolation. this adaptation enables the model to share common features across tasks while allocating distinct spaces for each task, thereby reducing interference. Our extensive experimental validation indicates that our framework surpasses other multi-task learning approaches, achieving joint optimization of tasks more effectively. this enhancement not only bolsters performance but also maintains an equilibrium between accuracy and inference speed. © 2024 IEEE.

关键词： Multi-task learning

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