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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441-450 订阅

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SynthRetailProduct3D (SyRePro3D): A Pipeline for Synthesis of 3D Retail Product models with Domain Specific Details Based on Package Class Templates 14th

SynthRetailProduct3D (SyRePro3D): A Pipeline for Synthesis o...

引用

14th international conference on Computer Vision Systems (ICVS)

作者： Lindermayr, Jochen Odabasi, Cagatay Voelk, Markus Chen, Yitian Bormann, Richard Huber, Marco F. Fraunhofer IPA Dept Robot & Assist Syst D-70569 Stuttgart Germany Univ Stuttgart Dept Cyber Cognit Intelligence CCI Fraunhofer IPA D-70569 Stuttgart Germany Univ Stuttgart Inst Ind Mfg & Management IFF D-70569 Stuttgart Germany

ISBN: (纸本)9783031441363;9783031441370

This research paper presents a novel pipeline for synthesizing high-quality, textured 3D models of retail products, addressing the limitations of existing datasets in the retail automation domain. As retail automation continues to revolutionize various aspects of business operations, there is a growing need for comprehensive and domain-specific datasets to enable the development and evaluation of robust perception systems. However, current datasets lack the necessary variety and size, often containing only a fraction of the products offered by retail companies and failing to capture domain-specific characteristics. To overcome these limitations, we introduce a modular pipeline that focuses on the synthesis of 3D models with high-resolution textures. Unlike previous methods that prioritize visually appealing shapes, our pipeline explicitly incorporates retail domain-specific details, including readable text, logos, codes, and nutrition tables, in the texture of the models. By disentangling the texturing process from shape augmentation, we ensure the visual quality of text areas, providing realistic and readable representations of retail products. In our methodology, we leverage state-of-the-art generative image models to add retail domain-specific details to the 3D models. Our approach enables the synthesis of diverse retail product packages with accurate textures, enhancing the realism and applicability of the generated models for the development and evaluation of perception systems in retail automation and robotics. Experimental results demonstrate the effectiveness and visual quality of models generated by our pipeline.

关键词： 3D Model Synthetic Retail automation robotics

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Toxicity Detection Using State of the Art Natural Language Methodologies 8

Toxicity Detection Using State of the Art Natural Language M...

引用

8th international conference on Control and robotics Engineering (ICCRE)

作者： Keskin, Enes Faruk Acikgoz, Erkut Dogan, Gulustan Turkish Aeronaut Assoc Univ Dept Comp Engn Ankara Turkiye Atilim Univ Dept Ind Engn Ankara Turkiye Univ North Carolina Wilmington Dept Comp Sci Wilmington NC USA

ISBN: (纸本)9798350345650

In this paper, the studies carried out to detect objectionable expressions in any text will be explained. Experiments were performed with Sentence transformers, supervised machine learning algorithms, and Bert transformer architecture trained in English, and the results were observed. To prepare the dataset used in the experiments, the natural language processing and machine learning methodologies of the toxic and non-toxic contents in the labeled text data obtained from the Kaggle platform are explained, and then the methods and performances of the models trained using this dataset are summarized in this paper.

关键词： language models bert transformers natural language processing supervised machine learning algorithms deep learning

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Control of a Soft Actuator using a Long Short-Term Memory Neural Network 26

Control of a Soft Actuator using a Long Short-Term Memory Ne...

引用

26th international conference on methods and models in automation and robotics

作者： Yanev, Victor Giannaccini, Maria Elena Aphale, Sumeet S. Univ Aberdeen Sch Engn Artificial Intelligence Robot & Mechatron Syst Gr Aberdeen Scotland

ISBN: (数字)9781665468589

ISBN: (纸本)9781665468596;9781665468589

Soft robots offer new opportunities because of their compliant physical structure and their wide range of applications. Currently the development of such robots is hampered by their low controllability. One of the main constituents of soft robots are soft actuators. The aim of this project is to improve the control of a non-linear system, the soft actuator, and its interaction with the environment, by training a long short-term memory (LSTM) neural network to accurately predict the actuator's position in space, its curvature, and the force applied by its end-effector on an external object. The increased performance of the trained network resulted in an error as low as 0.01 +/- 0.005 N in estimating the force applied by the end effector on the external object. The results show significantly superior performance (on the order of 10 times) in the positional and curvature predictions of the LSTM network when using one marker per air-chamber.

关键词： LSTM soft robotics control soft pneumatic actuator neural networks

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Research on Dataset Generation in the Development of Large Language models for Digital Textbooks 3

Research on Dataset Generation in the Development of Large L...

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3rd international conference on robotics, automation and Artificial Intelligence (RAAI)

作者： Lee, Youngho Daegu Natl Univ Educ Comp Educ Daegu South Korea

ISBN: (纸本)9798350307573;9798350307566

Recently, numerous institutions have been developing Large Language models (LLMs). This model is ushering in revolutionary changes in various fields including society, economy, and education. The LLM in education is expanding its use, and this expansion includes providing personalized learning experiences. However, the LLM currently being developed is a general model, rather than a model specialized for a specific subject or textbook. This may have limitations in its use by teachers and learners. Therefore, in this study, the LLM development, an open-source model, is being fine-tuned using a specific dataset. Before proceeding, it is necessary to develop a specific dataset. Human-generated datasets are expensive and subject-specific, thereby having disadvantages. Therefore, in this study, we propose a method of developing a textbook dataset by applying the self-instruct technique. It is expected that a textbook-specific dataset can be developed at a low cost through this.

关键词： LLMs Prompt Design Self-Instruct Data Generation Digital Textbook

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Quantitative Assessment of Lower-limb Motor Function Based on Multimodal Gait Data for Hip Osteoarthritis

Quantitative Assessment of Lower-limb Motor Function Based o...

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2024 lEEE international conference on Advanced Information, Mechanical Engineering, robotics and automation, AIMERA 2024

作者： Tong, Lina Li, Yunbo Wang, Chen Peng, Liang Huang, Yong China University of Mining and Technology-Beijing China Beijing China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing China Beijing Jishuitan Hospital Fourth Clinical College of Peking University Department of Orthopaedic Surgery Beijing China

ISBN: (纸本)9798350343335

The Harris Hip Score is very important in the clinical assessment of rehabilitation outcomes following Total Hip Arthroplasty (THA). Harris scales often have issues such as subjectivity and ceiling effects, it is difficult to measure the subtle changes in patients' disease progression. This paper proposes using deep learning algorithms to process multimodal gait data. First, conducting gait tests on THA patients to obtain optical motion capture data (MOC), surface electromyography data (sEMG), and force platform data (FP). Then, extracting features from these three modalities of data and input them into four models: LSTM, 1DCNN, 1DCNN-LSTM, and LSTM-1DCNN, to get the valuation results. Among these models, the 1DCNN-LSTM model performed best, with an L1Loss value of 9.952 and a correlation coefficient of 0.795 between the predicted values and the actual scores. This indicates that analyzing gait data with the 1DCNN-LSTM network can effectively predict Harris scores, which can provide a fundamental basis for the clinical diagnosis. © 2024 IEEE.

关键词： Arthroplasty

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Transferability of Machine Learning models for Energy Consumption Prediction of In-Vehicle Low-Voltage Equipment: A Case Study 20

Transferability of Machine Learning Models for Energy Consum...

引用

IEEE 20th international conference on automation Science and Engineering (CASE)

作者： Mueller, Julian Liu, Xuanheng Maier, Jonas Frey, Georg Mercedes Benz AG Sindelfingen Germany Univ Stuttgart Inst Automot Engn IFS Stuttgart Germany Saarland Univ Chair Automat & Energy Syst Saarbrucken Germany

ISBN: (纸本)9798350358513;9798350358520

As consumers become more concerned about environmental protection, automakers are accelerating the transition from traditional fuel vehicles to hybrid and fully electric vehicles. However, the relatively short driving range of contemporary electric vehicles and the resulting anxiety of running out of power still significantly inhibits the consumers' willingness to purchase them. Accurately predicting vehicle energy consumption has become a key factor in effectively alleviating consumers' range anxiety. This paper explores the use of Machine Learning methods combined with vehicle bus signal data to predict the low-voltage auxiliary component's consumption. It discusses the transferability of Machine Learning models between different car lines and different configurations from the perspectives of supervised and unsupervised learning. Additionally, we present a set of procedures for testing energy consumption of on-board low-voltage equipment, along with a workflow for efficient model transfer assessment. The research findings are expected to substantially decrease the data collection burden on developers and engineers, while expediting project implementation. Consequently, this will result in a more accurate range prediction tackling the consumers' range anxiety.

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Metal Surface Damages Detection Based-on Artificial Intelligence 3

Metal Surface Damages Detection Based-on Artificial Intellig...

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3rd international conference on robotics, Artificial Intelligence and Intelligent Control (RAIIC)

作者： Hu, Zetong Wuhan Univ Technol Wuhan Peoples R China

ISBN: (纸本)9798350352634;9798350352627

Based on the shortcomings of existing metal surface damages identification methods such as multiple limitations, and requiring a lot of manpower and resources, this paper intends to introduce deep learning algorithms into the field of steel surface damage identification, in order to find an efficient, simple, low-threshold and high-precision identification method for surface damages. After comparing existing convolutional neural network models such as VGG16, Resnet50, InceptionV3, DenseNet121, we optimized Resnet50 from the aspects of batchsize and optimizers. Based on the best settings and drawing on the structure of residual blocks in Resnet50, a new convolutional neural network model "Cbam Resnet" specifically designed for surface damage problem recognition was constructed. Finally, Cbam Resnet achieved an accuracy of 99.43% on the test set.

关键词： deep learning neural network models residual network attention mechanism

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Contactless Human Physical Signal Measurement for Collaborative Robotic Using Visible-IR Dual Cameras 29

Contactless Human Physical Signal Measurement for Collaborat...

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29th international conference on automation and Computing (ICAC)

作者： Li, Jiguang Yao, Wei Univ Salford North England Robot Innovat Ctr Salford Lancs England

ISBN: (纸本)9798350360882;9798350360899

Collaborative robotic configurations for monitoring and tracking human beings for safety and efficiency have attracted interest in industrial revolution. The fusion of different types of sensors embedded in collaborative robotic systems significantly improve robotic perception. However, current methods have not deeply explored the capabilities of multi-sensory configurations including visible and thermal sensors. In this paper, we propose a contactless multi-sensor fusion including visible and thermal dual camera for collaborative robots to improve the robotic perception for human safety. Remote photoplethysmography detection and infrared thermal camera were used to measure the heart rate and body temperature.

关键词： Heart rate Dual camera robotics Thermal camera Contactless

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Enhancing robotics Online 3D Bin Packing: A Comparative Study of Conventional Heuristic and Deep Reinforcement Learning Approaches 20

Enhancing Robotics Online 3D Bin Packing: A Comparative Stud...

引用

IEEE 20th international conference on automation Science and Engineering (CASE)

作者： Xiong, Heng Ding, Kai Ding, Wan Qiu, Xuchong Janschek, Klaus Xu, Jianfeng Huazhong Univ Sci & Technol Sch Mech Sci & Engn State Key Lab Intelligent Mfg Equipment & Technol Wuhan 430074 Peoples R China BOSCH Corp Res Beijing Peoples R China Tech Univ Dresden Dresden Germany

ISBN: (纸本)9798350358513;9798350358520

Robotic object packing holds a wide array of practical applications across logistics and manufacturing sectors. The online 3D Bin Packing Problem (BPP) is a challenging task that involves online packing of three-dimensional boxes into a container while considering constraints and objectives. Unlike the offline version, where all boxes are known in advance, the online variant requires decisions about how to pack each box as it arrives, without prior knowledge of upcoming boxes. To maximize space utilization, our study explores two distinct strategies: conventional heuristics-based algorithms and a deep reinforcement learning (DRL)-based approach. For the heuristic strategy, we propose four heuristic criteria alongside two variants of multi-objective optimization (MOO) algorithms. Quantitative experiments reveal that MOO outperforms singleobjective approaches for online bin packing. In our DRL-based approach, we introduce a framework that leverages a candidate map that indicates the potentially feasible placements, ensuring a balanced exploration and exploitation in the considerable discrete action space. Experiments demonstrate the superior performance of our DRL-based approach compared to both DRL-based baseline methods and conventional approaches. Additionally, we discuss the limitations of DRL-based methods and offer practical recommendations for real-world applications.

关键词： Deep reinforcement learning

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LLM2AutoML: Zero-Code AutoML Framework Leveraging Large Language models

LLM2AutoML: Zero-Code AutoML Framework Leveraging Large Lang...

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2024 international conference on Intelligent robotics and Automatic Control

作者： Chen, Sihan Zhai, Weihong Chai, Chen Shi, Xiupeng Shanghai Univ Sch Microelectron Shanghai Peoples R China Tongji Univ Coll Transportat Engn Shanghai Peoples R China

ISBN: (纸本)9798350389814;9798350389807

Automated Machine Learning (AutoML) aims to make machine learning accessible to non-experts by minimizing technical barriers and enabling the creation of high-performance models without extensive programming knowledge. Despite advances, existing AutoML solutions often struggle with complexity and inefficiency in handling intricate tasks. Herein, we present LLM2AutoML, a zero-code AutoML framework that leverages Large Language models (LLMs) to generate high-performance actionable ML models without coding and with explanations. LLM2AutoML enables bidirectional human-machine alignment by interpreting user intentions expressed in natural language, converting them into executable AutoML tasks, and delivering detailed analytical reports that foster user understanding and trust. We propose the template-bounded AutoML method to ensure the LLM-generated code is highly executable, enabling fully end-to-end automation, including intention parsing, model auto-selection, and hyperparameter auto-tuning. Additionally, we incorporate techniques such as adaptive loss functions and configuration recommendations to improve efficiency and performance. Experiments on the SECOM dataset from the semiconductor manufacturing industry demonstrate that LLM2AutoML enhances the automation and usability of AutoML and LLMs, achieves superior performance, and produces high-quality analytical reports. This framework presents a novel approach to advancing the effectiveness and capabilities of both AutoML and LLMs.

关键词： AutoML Large Language models (LLMs) Zero-Code Human-machine Bidirectional Alignment Hyperparameter Optimization

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