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

共 24757 条记录，以下是4511-4520 订阅

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Keypoints-guided Lightweight Network for Single-view 3D Human Reconstruction

Keypoints-guided Lightweight Network for Single-view 3D Huma...

引用

IEEE international conference on robotics and automation (ICRA)

作者： Yuhang Chen Chenxing Wang School of Automation Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University China

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Single-view 3D human reconstruction has been a hot topic due to the potential of wide applications. To achieve high accuracy, existing works usually take computationally intensive models as backbone for exhaustive underlying features and then directly estimate human mesh vertices. These factors lead to redundant parameters, large calculations and low efficiency, while lightweight solutions to address these challenges are relatively scarce. In this work, based on the problems studied above, we propose a keypoints-guided lightweight network with an encoding-decoding framework. As the input is an image, a lightweight backbone named multi-stage and global feature enhanced network is designed for 2D encoding, where some operations of multi-scale fusion and frequency domain filtering are performed to extract more informative but low-resolution features. As the output is mesh of human body, we construct a keypoints-based 3D human template, with which the 2D low-resolution features can be mapped to 3D space to guide the 3D decoding with high efficiency and high accuracy. Extensive experiments on popular benchmarks 3DPW and Human3.6M illustrate the favorable trade-off between the accuracy and complexity of our method. Our code is publicly available at https://***/ChrisChenYh/***.

关键词： Three-dimensional displays Accuracy Image coding Frequency-domain analysis Estimation Benchmark testing Feature extraction

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Day-Ahead Forecasting for the Tropics with Numerical Weather Prediction and Machine Learning

Day-Ahead Forecasting for the Tropics with Numerical Weather...

引用

international conference on Control, automation, robotics and Vision (ICARCV)

作者： Nigel Yuan Yun Ng Harish Gopalan Venugopalan S. G. Raghavan Chin Chun Ooi Department of Physics National University Singapore Singapore Department of Fluid Dynamics Institute of High Performance Computing Singapore Department of Fluid Dynamics Centre for Frontier AI Research Institute of High Performance Computing Singapore

ISBN: (纸本)9781665476881

Numerical weather prediction (NWP) and machine learning (ML) methods are popular for weather forecasting. However, NWP models have multiple possible physical parameterizations, which requires site-specific NWP optimization. This is further complicated when regional NWP models are used with global climate models, each with multiple possible parameterizations. In this study, a hybrid numerical-statistical approach is proposed and evaluated for four radiation models. Weather Research and Forecasting (WRF) model is run in both global and regional mode to provide an estimate for solar irradiance. This estimate is then post-processed using ML to provide a final prediction. Normalized root-mean-square error from WRF is reduced by up to 40-50% with this ML error correction model. Results obtained using CAM, GFDL, New Goddard and RRTMG radiation models were comparable after this correction, negating the need for WRF parameterization tuning. Other models incorporating nearby locations and an ensemble set-up are also evaluated, although they produced much smaller improvements.

关键词： Systematics Wind energy Weather forecasting Machine learning Predictive models Numerical models Error correction

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The Role of robotics: automation in Shoe Manufacturing

The Role of Robotics: Automation in Shoe Manufacturing

引用

IEEE/ASME international conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Pedro A. Dias Marcelo R. Petry Luís F. Rocha INESC TEC - Instituto de Engenharia de Sistemas e Computadores Tecnologia e Ciência Porto Portugal Instituto Superior de Engenharia do Porto Instituto Politécnico do Porto Porto Portugal

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

Emerging from a rich heritage, the shoe manufacturing industry stands as one of the world’s most enduring and tradition-bound sectors. While renowned for their high-quality craftsmanship, countries like Portugal and Italy share the spotlight with those who focus on mass production methods. Regardless of their manufacturing model, both must adapt to the evolving competitive landscape by embracing innovative manufacturing techniques. robotics has emerged as a transformative force within the shoe industry, offering a path towards enhanced working conditions for employees while simultaneously reducing reliance on manual labor and bolstering productivity. The main focus of this paper is the comprehensive literature review, which examines the advancements made by researchers in various stages of shoe production, including roughing, gluing, finishing, and lasting. This article sheds light on the industry’s response to modernization and efficiency imperatives, providing a thorough understanding of robotics in shoe manufacturing automation. A case study on the real implementation and simulation of a robotic cell for sole roughing is also presented. The results revealed that the robotic cell maintains the production cadence.

关键词： Employee welfare Productivity Footwear industry Costs Service robots Reviews Production Footwear Manufacturing Safety

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Building models of Chemical-Technological Processes and Obtaining Solutions Based on Soft Computing 4

Building Models of Chemical-Technological Processes and Obta...

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4th international conference on Control Systems, Mathematical Modeling, automation and Energy Efficiency, SUMMA 2022

作者： Gromov, Yuri Pogonin, Vasiliy Terekhova, Anastasiya Tambov State Technical University Department of Information Processes and Management Tambov Russia

ISBN: (数字)9781665456593

ISBN: (纸本)9781665456593

The need to formulate and solve problems of guaranteeing control of dynamic modes of chemical-technological processes (CTP) is due to the fact that in the classical case the solution to the Cauchy problem is one integral curve, and in the case when the Cauchy problem contains at least one fuzzy parameter, then the solution is already a set of integral curves, or 'bunches'. In this regard, methods are proposed for constructing envelopes of a 'bundle' of integral curves, which can be considered as a fuzzy set. In the case when it is possible to construct an analytical expression for the membership function, it is advisable to use the representation of fuzzy numbers based on it. © 2022 IEEE.

关键词： Soft computing

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Use of Machine Learning methods in the Assessment of Programming Assignments 25th

Use of Machine Learning Methods in the Assessment of Program...

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25th Annual international conference on Text, Speech and Dialogue conference (TSD)

作者： Tarcsay, Botond Vasic, Jelena Perez-Tellez, Fernando Technol Univ Dublin Dublin Ireland

ISBN: (纸本)9783031162701;9783031162695

Programming has become an important skill in today's world and is taught widely both in traditional and online settings. Educators need to grade increasing numbers of student submissions. Unit testing can contribute to the automation of the grading process;however, it cannot assess the structure, or partial correctness, which are needed for finely differentiated grading. This paper builds on previous research that investigated several machine learning models for determining the correctness of source code. It was found that some such models can be successful, provided that the code samples used for fitting and prediction fulfil the same sets of requirements (corresponding to coding assignments). The hypothesis investigated in this paper is that code samples can be grouped by similarity of the requirements that they fulfil and that models built with samples of code from such a group can be used for determining the quality of new samples that belong to the same group, even if they do not correspond to the same coding assignment, which would make for a much more useful predictive model in practice. The investigation involved ten different machine learning algorithms used on over four hundred thousand student code submissions and it confirmed the hypothesis.

关键词： Applied Machine Learning for Code Assessment Student Programming Code Grading Automated Grading

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Chunking Strategy for Retrieval Augmented Generation in Regulation Documents

Chunking Strategy for Retrieval Augmented Generation in Regu...

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IEEE international conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Amar Fadillah Nuke Athahirah Kuan Ting Lai College of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Industrial Automation and Robotics Engineering Education Universitas Pendidikan Indonesia Bandung Indonesia

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

Large language model (LLM) have been proven to be capable of performing various text processing tasks. LLMs have the potential to be used as a tool for analyzing regulation documents. To enhance the ability of LLMs to analyze regulation documents, we propose a regulation document chunking method that improves the accuracy of the analysis results. Our proposed method produces better accuracy than the sequential chunking method.

关键词： Visualization Accuracy Text analysis Codes Large language models Layout Regulation

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Cross-Project Software Defect Prediction Using Ensemble Model with Individual Data Balancing and Feature Selection 22nd

Cross-Project Software Defect Prediction Using Ensemble Mode...

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22nd international conference on Advances in Mobile Computing and Multimedia Intelligence, MoMM 2024

作者： Yakovyna, Vitaliy Nesterchuk, Oleh University of Warmia and Mazury in Olsztyn Oczapowskiego Str. 2 Olsztyn10-719 Poland Lviv Polytechnic National University Bandery Str. 12 Lviv79013 Ukraine

ISBN: (纸本)9783031780486

The quality of software significantly influences its safety and security. With the rapid expansion of software development, the issue of coding quality has become increasingly critical. The manual and resource-intensive nature of error detection in software and its inherent unreliability underscores the necessity for automation. Consequently, a burgeoning interest is employing machine learning methods for software defect prediction. This study introduces a novel stacking software cross-project defect prediction model. Each weak classifier undergoes a learning process incorporating individual data balancing and feature selection techniques. The efficacy of the model was evaluated using accuracy and F1 score metrics on multiple project datasets sourced from the PROMISE repository. The application of the proposed model yielded a classification accuracy of 0.839 and an F1 score of 0.909, surpassing the average performance of single classifiers. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Prediction models

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AIEngiMentor: Scalable Deep Learning System for Hands-on Engineering Education

AIEngiMentor: Scalable Deep Learning System for Hands-on Eng...

引用

Signal, Information and Data Processing (ICSIDP), IEEE international conference on

作者： Winfred Lin Qiang Wang Integem Inc. Cupertino CA USA

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In this research, we proposed AIEngiMentor, a scalable system that assists students by automatically detecting and diagnosing errors in robotics and engineering assembly. It integrates deep learning models for multi-stage video segmentation and recognition, leveraging the Segment Anything Model 2 (SAM2) to handle both device- and component-level complexities. AIEngiMentor incorporates a Video-based Adaptive Integrity Algorithm (VAIA), which dynamically adjusts to step-by-step requirements from the Learning Management System (LMS). This architecture delivers real-time, precise feedback, even when processing low-quality or moving images with complex backgrounds. Unlike costly industry solutions, AIEngiMentor provides an affordable, practical alternative, ensuring the accuracy and safety of circuit and device connections by validating each step against LMS-defined criteria. Preliminary results show AIEngiMentor outperforms manual inspection in accuracy and enhances student learning by enabling focus on complex problem-solving tasks. The system's adaptability and precision also indicate potential for broader use in industrial automation and healthcare, where real-time error detection is critical.

关键词： Deep learning Learning management systems Image segmentation Adaptation models Accuracy automation Medical services Real-time systems Problem-solving Wire

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Design and Implementation of a Portable Digital Energy Measurement Field Diagnosis Instrument Based on DBN 5

Design and Implementation of a Portable Digital Energy Measu...

引用

5th international conference on Electrical Engineering and Control Technologies, CEECT 2023

作者： Guo, Huizhu Duan, Yuqing Ding, Jianshun Zhou, Wen Li, Xinran Zhang, Wenqi Liu, Jun State Grid Anhui Marketing Service Center Hefei China Jiangsu LingChuang Electric Automation Co. Ltd Zhenjiang China

ISBN: (纸本)9798350342253

A portable digital energy metering on-site diagnostic instrument based on Deep Belief Network (DBN) is designed to address the issue of current on-site digital energy meters being unable to provide a complete sampling value model and accurately conduct optical power testing. Firstly, the hardware architecture and appearance structure plan of the diagnostic unit were developed through requirement analysis, and corresponding hardware equipment was developed. Then, a dual core (APU&RPU) software structure suitable for on-site diagnostic units was designed and developed separately. Finally, a deeper analysis and feature extraction of the relationship between metric data were carried out by introducing the DBN model, and high accuracy fault diagnosis was achieved. A comparative analysis was conducted between the designed diagnostic instrument and the other two methods under the same conditions through simulation experiments. The results show that the proposed method has the highest accuracy and Fl score in diagnosing 5 different types of faults, reaching 96.54% and 95.75% respectively, which is about 3% and 5% higher than the other two methods, respectively. Its performance is superior to the other three comparative models. © 2023 IEEE.

关键词： Fault detection

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AB-LSTM: Long Short Term Memory with Artificial Bee Colony for Predicting Coal Mine Gas Concentration

AB-LSTM: Long Short Term Memory with Artificial Bee Colony f...

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Industrial automation, robotics and Control Engineering (IARCE), international conference on

作者： Peng Wang Bin Liu Hongfei Zhao Zengrong Zheng Jun Wang Zhiqi Xu Lichun Shu Shengqiang Liu Yimin Sun Xiaobo Zhang Information Research Institute Ministry of Emergency Management Beijing China Shenmu Energy Administration Shenmu Shaanxi China Shenmu Information Industry Development Group Co. Ltd Shaanxi China School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu China

ISBN: (数字)9798350380323

ISBN: (纸本)9798350380330

The efficacy of gas concentration prediction is of utmost significance as it hinges on a well-defined theoretical foundation. Such a foundation is essential for bolstering preventive measures and facilitating the effective control of gas-related disasters within mining operations. The gas concentration prediction is carried out by integrating the improved Artificial Bee Colony algorithm, abbreviated as AB, with the long short term memory network (LSTM). Through the application of AB optimized LSTM, the prediction accuracy of LSTM is remarkably enhanced. The AB-LSTM gas concentration prediction model was established by meticulously selecting temperature, dust, and wind speed as the principal factors that have a profound impact on gas concentration and combining them with comprehensive laboratory test data. The outcomes obtained from this model vividly demonstrate its robust generalization capabilities. It is highly proficient in predicting both the evolving trends and peak values of gas concentration with remarkable precision. This proficiency not only powerfully underscores the model's reliability but also highlights its immense potential as a valuable and indispensable tool. It offers crucial theoretical insights and technical support that are vital for the comprehensive prevention and control of gas disasters in coal mines, thereby safeguarding the safety and well-being of miners and the overall stability of mining operations.

关键词： Adaptation models Disasters Neural networks Predictive models Artificial bee colony algorithm Prediction algorithms Data models Coal mining Long short term memory Optimization

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