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检索条件"任意字段=International Conference on Methods and Models in Automation and Robotics"

共 24666 条记录，以下是341-350 订阅

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An Abnormal Detection Method of Electric Power Equipment Based on Contrast Learning

An Abnormal Detection Method of Electric Power Equipment Bas...

引用

Electrical automation and Artificial Intelligence (ICEAAI), international conference on

作者： Kunsan Zhang Chen Chen Xiaoping Lin Siqi Pan Linyun Fan Jinhuang Lin State Grid Fujian Electric Power Co. Ltd. Zhangzhou Power Supply Company Zhangzhou China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

With the widespread application of Industrial Control Systems (ICS), equipment anomaly detection has become crucial for ensuring safety and reliability. Traditional detection methods rely on predefined rules and thresholds, which often lack accuracy and adaptability when faced with complex network attacks and equipment failures. To address this issue, this paper proposes an anomaly detection method based on contrastive learning. By incorporating data augmentation techniques (such as timestamp perturbation, field masking, noise addition, etc.), the method reduces reliance on large amounts of labeled data and enhances model robustness. Additionally, deep neural networks (such as CNNs and Transformers) are used for feature extraction, enabling the model to capture more complex anomaly patterns. The paper also combines classification loss with contrastive loss, improving the model's ability to distinguish between normal and abnormal samples in the feature space, thus enhancing detection accuracy and reliability. Experimental results demonstrate that the proposed method performs well in anomaly detection tasks within industrial control systems, showing promising potential for real-world applications.

关键词： Adaptation models Accuracy Industrial control Contrastive learning Feature extraction Transformers Data augmentation Data models Safety Anomaly detection

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Multi-Time Scale Simulation of Integrated Energy System Based on Finite Difference Approximation

Multi-Time Scale Simulation of Integrated Energy System Base...

引用

Electrical automation and Artificial Intelligence (ICEAAI), international conference on

作者： Wentao Yang Aochen Jin Hao Sun Wenbin Yang Guangxi University College of Electrical Engineering Nanning China The R&D Department II China Mobile Industry Research Institute Chengdu China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

The time scale span of energy transmission in the electric gas thermal integrated energy system is enormous, and the mathematical modeling of dynamic processes in multi-energy flow systems at different time scales is very complex. The simulation efficiency and accuracy of multi-energy flow systems are difficult to balance, and the accurate characterization of system response faces great challenges. In response to the above issues, this paper proposes a multi-time scale simulation method of integrated energy systems based on finite difference approximation. Firstly, the partial differential equations of gas and heat pipelines are discretized using the finite difference approximation theory, and a dynamic model involving time delay in gas and heat pipelines is established. Secondly, according to the breadth-first traversal algorithm, network constraints are met to connect coupled devices and networks. Finally, the simulation of the real operating state of the system when encountering small disturbances or faults verifies that the dynamic model proposed in this paper can significantly reduce the complexity and computational volume of the model, and improve simulation efficiency while ensuring accuracy.

关键词： Heating systems Solid modeling Analytical models Accuracy Computational modeling Pipelines Mathematical models Computational efficiency Complexity theory Finite difference methods

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Analysis of Fractional-Order Biological Model via A Novel Semi-Analytical Approach

Analysis of Fractional-Order Biological Model via A Novel Se...

引用

Advancements in Electrical, Electronics, Communication, Computing and automation (ICAECA), international conference on

作者： Jayaram Prakash K Saratha S R Department of Mathematics Kumaraguru College of Technology Coimbatore Tamil Nadu India

ISBN: (数字)9798331525439

ISBN: (纸本)9798331525446

This study presents a hybrid methodology that combines a novel fractional integral transform with the Fractional Differential Transform Method (FDTM) to solve fractional differential equations (FDEs). The proposed approach exploits the transform’s ability to handle fractional-order systems efficiently by improving solution convergence and preserving Mittag-Leffler-type behavior. The new method provides solutions for both linear and nonlinear problems with reduced computational effort compared to other techniques such as the Adomian Decomposition Method and Variational Iteration Method, which require multiple integrations and differentiations. In contrast, the proposed method avoids such computations by utilizing the integral transform. The efficacy of this hybrid approach is demonstrated through the analysis of a fractional-order prey-predator model with a single carrying capacity. Series solutions for various fractional orders are obtained and validated through numerical comparison with existing methods, showing that the proposed technique achieves similar accuracy while requiring fewer computational steps. Graphical illustrations highlight its practical utility in solving non-linear FDEs, underscoring the advantages of the hybrid methodology in fractional modeling.

关键词： Analytical models Electric potential Computational modeling Biological system modeling Integral equations Transforms Differential equations Numerical models Telecommunication computing Convergence

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Continuous Adversarial Text Representation Learning for Affective Recognition

Continuous Adversarial Text Representation Learning for Affe...

引用

international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Seungah Son Andres Saurez Dongsoo Har CCS Graduate School of Mobility Korea Advanced Institute of Science and Technology (KAIST) Daejeon Rep. of Korea Robotics Program Korea Advanced Institute of Science and Technology (KAIST) Daejeon Rep. of Korea

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

While pre-trained language models excel at semantic understanding, they often struggle to capture nuanced affective information critical for affective recognition tasks. To address these limitations, we propose a novel framework for enhancing emotion-aware embeddings in transformer-based models. Our approach introduces a continuous valence-arousal labeling system to guide contrastive learning, which captures subtle and multi-dimensional emotional nuances more effectively. Furthermore, we employ a dynamic token perturbation mechanism, using gradient-based saliency to focus on sentiment-relevant tokens, improving model sensitivity to emotional cues. The experimental results demonstrate that the proposed framework outperforms existing methods, achieving up to 15.5% improvement in the emotion classification benchmark, highlighting the importance of employing continuous labels. This improvement demonstrates that the proposed framework is effective in affective representation learning and enables precise and contextually relevant emotional understanding.

关键词： Representation learning Affective computing Sentiment analysis Sensitivity Text recognition Perturbation methods Semantics Contrastive learning Benchmark testing Transformers

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Anything-DT Modeling: A New Framework for Digital Twin Modeling of Wild Objects from a Single RGB Image

Anything-DT Modeling: A New Framework for Digital Twin Model...

引用

Electrical automation and Artificial Intelligence (ICEAAI), international conference on

作者： Jun Geng Siqi Wang Xiaoyun Yi Wanqing Xiong Hongchao Yin Xiaoyu Li The School of Software Xinjiang University Urumqi China The School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

Given the significant limitations of current digital twin modeling methods in practical applications, their lack of sufficient generalizability, and the inherent diversity and complexity of real-world objects and environments, digital twin modeling from a single RGB image in unconstrained scenarios faces several maj or challenges. In this paper, we first improve the Segment Anything Model (SAM) and propose the CSAM model, which enables more accurate segmentation of any object. Next, we introduce the Anything-DT Modeling framework, which cleverly integrates the CSAM object segmentation model with a vision-language model to accomplish digital twin modeling tasks using only a single image. The CSAM model can maintain high accuracy and efficiency in complex scenarios, providing high-quality input data for subsequent digital twin modeling. The framework fully utilizes the latest advances in computer vision and natural language processing, providing strong technical support for digital twin modeling, thereby ensuring the feasibility of digital twin modeling from a single RGB image. Compared with existing state-of-the-art methods, the Anything-DT Modeling framework demonstrates its ability to produce detailed and accurate digital twin models for a wide range of objects, showcasing promising potential in overcoming the limitations of current approaches.

关键词： Image segmentation Visualization Solid modeling Accuracy Three-dimensional displays Computational modeling Text to image Data models Digital twins Faces

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3D Industrial Human Action Recognition Based on the Synergy Between Convolutional Neural Networks and Long Short-Term Memory

3D Industrial Human Action Recognition Based on the Synergy ...

引用

international conference on Autonomous Robots and Agents, ICARA

作者： Mohammadamin Salimi Carla Alves André Cardoso Estela Bicho Luis Louro Ana Colim José J. M. Machado João Manual R. S. Tavares Duarte Fernandes DTx - Digital Transformation Colab Azurém Campus of University of Minho Guimarães Portugal Dept of Industrial Eletronics Algoritmi Center Azurém Campus of University of Minho Guimarães Portugal DTx-Digital Transformation Colab Algoritmi Centre Azurém Campus of University of Minho Guimarães Portugal Departamento de Engenharia Mecânica Faculdade de Engenharia Universidade do Porto Porto Portugal DTx-Digital Transformation Colab Azurém Campus of University of Minho Guimarães Portugal

ISBN: (数字)9798331509231

ISBN: (纸本)9798331509248

This paper illustrates a significant contribution to Human Activity Recognition (HAR) within industrial environments. We propose a deep learning model that combines Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) with Recurrent Neural Networks (RNN), offering a superior method for processing a novel and multicamera dataset. The proposed LSTM-Conv1D and RNN models achieved classification accuracies of 92% and 87%, respectively, illustrating the effectiveness of the LSTM-Conv1D architecture in recognizing human actions with higher precision compared to the RNN model. This paper focuses on the HAR based on generating a comprehensive dataset with joints cartesian position data for 3D motion analysis, specifically emphasizing the upper segments within macro action movements.

关键词： Deep learning Recurrent neural networks Three-dimensional displays automation Accuracy Service robots Motion segmentation Human activity recognition Convolutional neural networks Long short term memory

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Multi-Discriminator Deep Learning Framework for Comprehensive Classification Tasks

Multi-Discriminator Deep Learning Framework for Comprehensiv...

引用

Electronics, Energy Systems and Power Engineering (EESPE), IEEE international conference on

作者： Haorun Zhao Tianjin University of Science and Technology Tianjin China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper presents an automated classification model leveraging multiple artificial intelligence discriminators to tackle complex classification tasks in industrial and agricultural scenarios. Traditional manual classification methods are often inefficient and susceptible to subjective biases. The proposed approach integrates deep learning techniques to extract multidimensional features of objects, combining discriminators such as decision trees, support vector machines, and random forests, to achieve high accuracy and efficiency. The study begins with dataset preprocessing and correlation analysis, followed by the construction of decision tree and random forest models, and concludes with parameter optimization to enhance classification performance. Experimental results show that the random forest model achieves superior performance with an accuracy of 84.07 % and an AUC value of $\mathbf{0. 9 6}$ . Furthermore, comparative analysis with other classifiers highlights the effectiveness of the proposed model. This research provides valuable insights and technical support for advancing intelligence and automation in various industrial applications.

关键词： Deep learning Support vector machines Analytical models Adaptation models Accuracy Correlation Scalability Feature extraction Decision trees Random forests

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Research on Intelligent Evaluation Model of Classroom Teaching Based on Machine Learning Algorithm

Research on Intelligent Evaluation Model of Classroom Teachi...

引用

Electronics, Energy Systems and Power Engineering (EESPE), IEEE international conference on

作者： Caiyun Wang Changchun University of Architecture and Civil Engineering Changchun China

ISBN: (数字)9798350389579

ISBN: (纸本)9798350389586

This paper proposes an intelligent evaluation model of classroom teaching based on SVM algorithm, aiming to improve the automation, objectivity and accuracy of the evaluation process. First, this paper designs an intelligent model framework suitable for classroom teaching evaluation, and combines machine learning technology to collect, extract features and analyze teaching data. By using SVM, the model can be used to evaluate the effectiveness of classroom instruction. In the course of the experiment, people used the multi-dimension teaching data, including the interaction in class, the homework, the test results, and so on. The experimental results show that the proposed model can effectively evaluate the teaching quality of teachers and the learning status of students. The accuracy of model prediction reached 89.5%, which is 15% higher than that of traditional manual evaluation methods. In addition, the evaluation model based on SVM shows strong robustness and adaptability in different teaching scenarios, and can quickly adapt to changes in the teaching environment.

关键词： Support vector machines Adaptation models Machine learning algorithms Accuracy Heuristic algorithms Education Evaluation models Feature extraction Data models Robustness

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River-GEM: Generating and Enhancing Muddy Water Images

River-GEM: Generating and Enhancing Muddy Water Images

引用

international conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Alik Pramanick Chivukula Sairam Satwik Sonal Kumar Akshay Daydar Arijit Sur Indian Institute of Technology Guwahati India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Underwater image enhancement is crucial for marine engineering and aquatic robotics. However, most recent methods have focused on ocean environments, where they trained and tested on oceanic images. As a result, these methods are less effective in river water, where relatively blurry images are produced due to extensive muddy environments. In river water-based image restoration, we have observed two main limitations: (1) the lack of datasets that accurately represent the muddy water conditions found in river environments and (2) the limited effectiveness of current enhancement models in dealing with the unique challenges of muddy water images. To address these limitations, we introduce the "River-GEM" framework, which includes (1) a dataset of highly degraded muddy water images generated using a principle component analysis-based fusion technique and (2) a novel enhancement model utilizing the depth-map guidance to learn complex contextual details of muddy water images. The proposed model captures the intricate details of the objects in degraded images by analyzing feature correlation at multiple scales. Our model shows improvements of 3.42% in SSIM and 5.04% in PSNR over existing methods on the muddy-UIEB dataset. This dataset and enhancement network marks a significant step forward in this area of research. The dataset and code will be available at: https://***/Alik033/River-GEM.

关键词： Degradation Analytical models Oceans Speech enhancement Signal processing Marine engineering Rivers Image restoration Robots Image enhancement

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LLM-Driven Peer Competitor and Stock Analysis

LLM-Driven Peer Competitor and Stock Analysis

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Computing for Sustainability and Intelligent Future (COMP-SIF), international conference on

作者： Sharathchandra Patil S. Mahalakshmi N R Vikas V N Srajan M Srinandan Information Science & Engineering BMS Institute of Technology & Management Bengaluru India

ISBN: (数字)9798331538538

ISBN: (纸本)9798331538545

The competitive landscape is a significant factor in strategic corporate decision-making, and therefore the identification of peer competitors is crucial for businesses, investors, and financial analysts. This paper introduces an automated system that utilizes Large Language models (LLMs) and financial data retrieval methods to improve competitor identification for U.S. firms. By combining company descriptions from Wikipedia with financial information retrieved through APIs, the suggested system greatly enhances the effectiveness of market analysis and competitive intelligence. The system offers useful insights to corporations for strategic planning and investors for making smart decisions. automation of competitor identification saves manual labor along with offering accurate and real-time data availability, thus making it a scalable solution specific to the U.S. financial market.

关键词： Large language models Manuals Encyclopedias Strategic planning Competitive intelligence Real-time systems Internet Online services Sustainable development Stock markets

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