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

共 24667 条记录，以下是261-270 订阅

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A Review of Degradation Prediction methods for Proton Exchange Membrane Fuel Cells

A Review of Degradation Prediction Methods for Proton Exchan...

引用

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

作者： Jun Ma Xiaoqian Liu Ziheng Wu Rui Shen Haining Liu Fajia Li AECC Hunan Aviation Powerplant Research Institute Zhuzhou Hunan China School of mechanical Engineering University of Jinan Jinan China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

Proton exchange membrane fuel cells (PEMFCs), as a new type of clean and pollution-free power generation device, have the advantages of high energy conversion efficiency, low noise and fast start-up. However, the operation of PEMFCs is affected by multiple factors, and degradation problems are inevitable. Such degradation issues may cause safety hazards, reduce energy conversion efficiency and shorten their service life. Therefore, accurately predicting the degradation process is crucial for developing reliable and durable PEMFCs. This study reviews fuel cell degradation prediction models from three perspectives: model-based, data-driven, and fusion approaches. The study mentioned above is finally discussed.

关键词： Degradation Protons Reviews Computational modeling Noise Fuel cells Predictive models Hazards Reliability Power generation

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Data Mining Techniques for Predicting Energy Demand in Smart Grids: Optimizing Consumption and Production

Data Mining Techniques for Predicting Energy Demand in Smart...

引用

automation and Computation (AUTOCOM), international conference on

作者： Palvi Soni Gajendra Tandan Department of CS & IT Kalinga University Raipur India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The growing complexity of renewable energy systems and the smarter grid has also made energy management a major issue. Precise forecasting of energy demand remains one of the most critical factors for effective energy utilization and generation, concern, dependability, and cost-efficiency. In this research, we present state-of-art data mining methods for predicting the energy demand in smart grids. In view of this, this study uses decision tree, support vector machines and neural network to establish models from historical energy consumption data as well as weather and user behavior patterns to predict the consumption accurately. The authors proposed architecture is meant to work with large streams of data in real-time to support dynamic control of energy loads and other demand response methodologies. Additionally, the research measures the effectiveness of feature reduction, dimension reduction, and clustering in enhancing the energy plans' predictive models. These methods do not only improve predictive capability but also help in designing energy distribution and storage systems. The results emphasize on the necessity of context-specific data mining solutions for digital smart grids, managing their specific problems. This study will be useful to energy suppliers and governments to build foundations for effective and practicable energy utilization in the smart grid environment.

关键词： Support vector machines Energy consumption Accuracy Computational modeling Predictive models Feature extraction Real-time systems Smart grids Data mining Forecasting

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Work in Progress: VR-based robotics Training for AEC Industry

Work in Progress: VR-based Robotics Training for AEC Industr...

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IEEE World Engineering Education conference (EDUNINE)

作者： Biayna Bogosian Shahin Vassigh Bhavleen Kaur Narula Seth Corrigan Giancarlo Perez Mohammadreza Akbari Lor Bhanu Vodinepally Tisa Islam Erana Mark Alan Finlayson Shu-Ching Chen The Design School Arizona State University Tempe AZ USA Department of Architecture Florida International University Miami FL USA School for Inf. and Computer Sci. University of California Irvine CA USA School of Science and Engineering University of Missouri Kansas City MO USA School of Computer and Inf. Sci. Florida International University Miami FL USA

ISBN: (数字)9798331542788

ISBN: (纸本)9798331542795

This paper introduces the Intelligent Learning Platform for robotics Operations (IL-PRO), a Virtual Reality (VR) system designed to enhance robotics training in the Architecture, Engineering, and Construction (AEC) industry. IL-PRO addresses the growing need for effective training methods as the AEC sector adopts robotic automation. The system integrates VR technology with game-assisted learning, combining online multimedia lessons for theory with immersive VR tasks for practical skills. Developed iteratively using Design-Based Research principles, IL-PRO incorporates realistic robot simulations and progressive task complexity. The VR environment, built in Unity, aims to enhance engagement, motor coordination, and spatial awareness in robotics training. While future goals include AI-driven personalized instruction, this work-in-progress focuses on VR curriculum development and implementation. The paper concludes by discussing future directions, including curriculum expansion and cross-institutional adoption, to establish new benchmarks in innovative robotics education for the AEC industry.

关键词： Training Industries Solid modeling Service robots Robot kinematics Multimedia systems Curriculum development Motor coordination Engineering education Immersive learning

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Leveraging Artificial Intelligence for Colon Cancer: Trends, Challenges and Innovations

Leveraging Artificial Intelligence for Colon Cancer: Trends,...

引用

automation and Computation (AUTOCOM), international conference on

作者： Sakshi Takkar Manik Rakhra Dept. of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Artificial Intelligence (AI) has made huge enhancements in colon cancer research through its methods for diagnosis, treatment, and custom detection of one of the most common and fatal cancers suffered globally. Through AI deep learning and machine learning methods, tremendous possibilities have been realized in automating, for example, polyp detection in colonoscopy images, cancer classification from histopathological slides, and even predicting disease treatment outcomes. The current direction is towards transfer learning, hybrid model building and AI applications in surgery robots aimed at more accurate diagnosis and treatment procedures. Besides them, however, they have serious disadvantages, access to computer learning systems and datasets are limited, AI models come without explanation, embedding of artificial intelligence into clinical practices becomes troublesome. In future development, emphasize should be on creating databases, explainable ethics AI approaches, building bridges between developers and practitioners in medicine. The objective of the paper is how to incorporate research that has been done in moving colon cancer AI systems into practice, understanding the AI satellites put on colon cancer research.

关键词： Deep learning Ethics Technological innovation Computational modeling Buildings Transfer learning Surgery Colorectal cancer Data models Artificial intelligence

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LLM-Based Video Analytics Test Scenario Generation in Smart Cities

LLM-Based Video Analytics Test Scenario Generation in Smart ...

引用

international Scientific-Professional conference on Information Technology (IT)

作者： Merve Yilmazer Mehmet Karakose Computer Engineering Munzur University Tunceli Turkiye Computer Engineering Firat University Elazig Turkiye

ISBN: (数字)9798331517649

ISBN: (纸本)9798331517656

Rapid advances in the field of artificial intelligence have made significant contributions to the automation of software development and testing stages. Software created for use in various fields is tested with test scenarios created manually by software test experts or using test automation. Testing large-scale software with these methods complicates the testing phases because it requires increased human intervention and includes complex applications. In this study, an LLM-based scenario generation framework enhanced with prompt engineering is proposed for testing software to be used for video analysis in smart cities and smart campus areas. Thus, software test scenarios are created by strengthening large language models that are fast, flexible and have high learning ability using prompt engineering techniques. Test scenarios produced through LLM reinforced with prompt engineering techniques were evaluated with rarity and reality metrics and it was determined that more robust scenarios were produced compared to randomly generated test scenarios in the relevant field.

关键词： Software testing Measurement automation Smart cities Large language models Visual analytics Software Prompt engineering Scenario generation Software development management

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GoNoGo: An Efficient LLM-Based Multi-agent System for Streamlining Automotive Software Release Decision-Making 36th

GoNoGo: An Efficient LLM-Based Multi-agent System for Stream...

引用

36th international conference on Testing Software and Systems

作者： Khoee, Arsham Gholamzadeh Yu, Yinan Feldt, Robert Freimanis, Andris Rhodin, Patrick Andersson Parthasarathy, Dhasarathy Chalmers Univ Technol Dept Comp Sci & Engn Gothenburg Sweden Volvo Grp Gothenburg Sweden

ISBN: (纸本)9783031808883;9783031808890

Traditional methods for making software deployment decisions in the automotive industry typically rely on manual analysis of tabular software test data. These methods often lead to higher costs and delays in the software release cycle due to their labor-intensive nature. Large Language models (LLMs) present a promising solution to these challenges. However, their application generally demands multiple rounds of human-driven prompt engineering, which limits their practical deployment, particularly for industrial end-users who need reliable and efficient results. In this paper, we propose GoNoGo, an LLM agent system designed to streamline automotive software deployment while meeting both functional requirements and practical industrial constraints. Unlike previous systems, GoNoGo is specifically tailored to address domain-specific and risk-sensitive systems. We evaluate GoNoGo's performance across different task difficulties using zero-shot and few-shot examples taken from industrial practice. Our results show that GoNoGo achieves a 100% success rate for tasks up to Level 2 difficulty with 3-shot examples, and maintains high performance even for more complex tasks. We find that GoNoGo effectively automates decision-making for simpler tasks, significantly reducing the need for manual intervention. In summary, GoNoGo represents an efficient and user-friendly LLM-based solution currently employed in our industrial partner's company to assist with software release decision-making, supporting more informed and timely decisions in the release process for risk-sensitive vehicle systems.

关键词： LLMs LLM-based Multi-agent Software Release Assistant Table Analysis automation Risk-sensitive Systems

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Detection of Endangered Deer Species Using UAV Imagery: A Comparative Study between Efficient Deep Learning Approaches

Detection of Endangered Deer Species Using UAV Imagery: A Co...

引用

international conference on Unmanned Aircraft Systems (ICUAS)

作者： Agustin Roca Gastón Castro Gabriel Torre Leonardo J. Colombo Ignacio Mas Javier Pereira Juan I. Giribet Artificial Intelligence and Robotics Laboratory (LINAR) Universidad de San Andrés Victoria Provincia de Buenos Aires Argentina Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina Centre for Automation and Robotics (CSIC-UPM) Ctra. M300 Campo Real Km 0200 Arganda del Rey Madrid Spain Museo Argentino Bernardino Rivadavia Capital Federal Argentina

ISBN: (数字)9798331513283

ISBN: (纸本)9798331513290

This study compares the performance of state-of-the-art neural networks including variants of the YOLOv11 and RT-DETR models for detecting marsh deer in UAV imagery, in scenarios where specimens occupy a very small portion of the image and are occluded by vegetation. We extend previous analysis adding precise segmentation masks for our datasets enabling a fine-grained training of a YOLO model with a segmentation head included. Experimental results show the effectiveness of incorporating the segmentation head achieving superior detection performance. This work contributes valuable insights for improving UAV-based wildlife monitoring and conservation strategies through scalable and accurate AI-driven detection systems.

关键词： YOLO Deep learning Training Image segmentation Head Wildlife Neural networks Vegetation mapping Autonomous aerial vehicles Monitoring

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Comparative Study of Deep and Machine Learning models in Knee Osteoarthritis Detection

Comparative Study of Deep and Machine Learning Models in Kne...

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automation and Computation (AUTOCOM), international conference on

作者： Vaidhi Kapoor Makul Mahajan Salil Batra School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

The knee osteoarthritis (KOA) is a common type of arthritis characterised by recent cartilaginous joint destruction and alteration of bone, ligaments and synovial fluid. This paper presents a brief knowledge about the knee OA, including its cause and effect, assessment, signs, prevalence, and the methods of managing it together with Different algorithms like Decision Tree, Random Forest, Artificial Neural Network (ANN) Naive Bayes and CNN. The dataset was collected from GitHub which was from OAI dataset which consists of csv datasets. The accuracy of the algorithms were 85%, 89%, 88%, 95% and 85%. Out of five algorithms, the best algorithm were Naïve Bayes and Random Forest algorithm.

关键词： Support vector machines Machine learning algorithms Magnetic resonance imaging Artificial neural networks Classification algorithms Bayes methods Osteoarthritis Ligaments Random forests Software development management

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Research on Mechanical automation Control based on Bidirectional Long Short-Term Memory

Research on Mechanical Automation Control based on Bidirecti...

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Intelligent Systems and Computational Networks (ICISCN), international conference on

作者： Xiaolu Song College of Mechanical and Electrical Engineering Zaozhuang Vocational College of Science & Technology China

ISBN: (数字)9798331529246

ISBN: (纸本)9798331529253

The rapid advancement of industrial automation has led to increased reliance on intelligent control systems for optimizing mechanical processes. Traditional automation methods often struggle with dynamic environmental conditions, system uncertainties, and real-time decision-making challenges. Many existing approaches, such as PID controllers and conventional neural networks, fail to effectively handle sequential dependencies in time-series data, resulting in suboptimal system performance. To address these limitations, this research presents an advanced mechanical automation control framework based on a Bidirectional Long Short-Term Memory (BiLSTM) algorithm. The BiLSTM model enhances automation efficiency by capturing long-range dependencies in sequential data, improving predictive accuracy and system adaptability. By leveraging bidirectional processing, the model effectively learns past and future context, leading to superior performance in control precision and response time. Experimental evaluations demonstrate that the proposed framework achieves an accuracy of 98.2%, significantly outperforming conventional automation control techniques in terms of robustness, adaptability, and computational efficiency. This study contributes to the development of intelligent, data-driven mechanical automation systems that align with Industry 4.0 objectives and enhance operational reliability.

关键词： Adaptation models automation Accuracy Uncertainty Decision making Bidirectional long short term memory Process control Real-time systems Time factors Intelligent control

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Diff-OD: Object Detection with Text-to-Image Diffusion models

Diff-OD: Object Detection with Text-to-Image Diffusion Model...

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

作者： Baolin Chen Tianyu Li School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan China

ISBN: (数字)9798331506797

ISBN: (纸本)9798331506803

Diffusion models have recently made revolutionary breakthroughs in the task of generating images from text, which has aroused many works in transferring implicit knowledge in diffusion models to visual tasks. Although these studies have found that the visual representations of the intermediate layers of diffusion models are distinguishable, they mainly focus on tasks such as semantic segmentation and depth estimation, and have not yet explored the basic visual task of object detection. Inspired by the above findings, we set out to explore the application prospects of diffusion models in visual feature object detection tasks. We found that the visual features of diffusion models are both semantic and positional, achieving excellent visual recognition results in object detection tasks. Inspired by this, we designed scene text prompts to introduce more semantic information to the diffusion model to further enhance its detection ability. Since there is a strong positional connection between the conditional text prompt and the feature map in the pre-trained text-to-image diffusion model, we concatenate the cross-attention maps with diffusion features to further enhance its representation ability. Our method surpasses the methods based on ResnetlOl and Clip as feature extractors, and achieves comparable performance with Convnext, which is currently the most advanced visual backbone.

关键词： Representation learning Visualization Semantic segmentation Depth measurement Semantics Text to image Object detection Diffusion models Feature extraction Visual perception

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