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检索条件"任意字段=18th International Conference on Control Automation Robotics and Vision"

共 5313 条记录，以下是831-840 订阅

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KI-Net: AI-Based Optimization in Industrial Manufacturing—A Project Overview 18th

KI-Net: AI-Based Optimization in Industrial Manufacturing—...

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18th international conference on Computer Aided Systems theory, EUROCAST 2022

作者： Freudenthaler, Bernhard Martinez-Gil, Jorge Fensel, Anna Höfig, Kai Huber, Stefan Jacob, Dirk Software Competence Center Hagenberg GmbH Softwarepark 32a Hagenberg4232 Austria Innsbruck University of Innsbruck Technikerstraße 21a Innsbruck6020 Austria Rosenheim Technical University of Applied Sciences Hochschulstraße 1 Rosenheim83024 Germany Salzburg University of Applied Sciences Urstein Süd 1 Puch/Salzburg5412 Austria University of Applied Sciences Kempten Bahnhofstraße 61 Kempten87435 Germany

ISBN: (纸本)9783031253119

Artificial intelligence (AI) is a crucial technology of industrial digitalization. Especially in the production industry, a great potential is present in optimizing existing processes, e.g., concerning resource consumption, emission reduction, process and product quality improvements, predictive maintenance, and so on. Some of this potential is addressed by methods of industrial analytics beyond specific production technology. Furthermore, particular technological aspects in production systems address another part of this potential, e.g., mechatronics, robotics and motion control, automation systems, and so on. the problem is that the field of AI includes many research areas and methods, and many companies are losing the overview of the necessary and appropriate methods for solving the company problems. the reasons for this are, on the one hand, a lack of expertise in AI and, on the other hand, high complexity and risks of use for the companies (especially for SMEs). As a result, many potentials cannot yet be exploited. the KI-NET project aims to fill this gap, whereby a project overview is presented in this contribution. © 2022, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： robotics

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Depth-Awareness Shared Self-Supervised Bronchial Orifice Segmentation for Center Detection in vision-Based Robotic Bronchoscopy

Depth-Awareness Shared Self-Supervised Bronchial Orifice Seg...

引用

IEEE international conference on Cyber Technology in automation, control, and Intelligent Systems

作者： Siang Xu Xiangyu Wang Yanding Qin Hongpeng Wang Ningbo Yu Jianda Han Institute of Robotics and Automatic Information System Nankai University Tianjin China Institute of Intelligence Technology and Robotic Systems Shenzhen Research Institute of Nankai University Shenzhen China

ISBN: (数字)9798331506056

ISBN: (纸本)9798331506063

Limited by the complex porous structure of the bronchi, supervised segmentation of the bronchial orifice is difficult to perform. Since there is no gold standard for definition of the edges of bronchial orifices, manual labeling can lead to inconsistencies in calibration standards. this paper presents a self-supervised bronchial orifice segmentation method based on depth information and synthetic data. the method uses synthetic data and synthetic depth maps to train CNN and can be directly applied to real image data. We also propose an Attention Guidance Module that uses depth information to guide the neural network attention distribution. We manually segmented the bronchial orifice region as ground truth and counted the offset of the center point between the segmentation result and ground truth. the results show that the mIoU of the segmentation result is 79.52, the dice coefficient of the result is 0.86 which is better than 0.77 of the previous method, and the pixel deviation of the center point in a 128 × 128 image is 3.92 which is better than 4.35 of the previous method.

关键词： Bronchoscopy Image segmentation Neural networks Orifices Phantoms Manuals Respiratory system Robots Standards Synthetic data

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Autonomous Research Platform for Cleaning Operations in Mixed Indoor & Outdoor Environments

Autonomous Research Platform for Cleaning Operations in Mixe...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Shuo Sun Tangyike Zhang Zhihai Xiang Yuhang Han Dongen Li Jianghao Li Zhiyang Liu Marcelo H. Ang Department of Mechanical Engineering National University of Singapore Singapore Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University Xi'an Shaanxi China

ISBN: (纸本)9781665476881

In recent years, following the advances in autonomous driving technology, low-speed autonomous service vehicles such as delivery, patrolling, and road-cleaning vehicles have started to emerge. As a promising future cleaning solution, autonomous cleaning vehicles are expected to address the workforce shortage many countries face in the near future. this paper describes the detailed design of an autonomous research platform for cleaning operations in mixed indoor & outdoor environments. An electric manual vacuum sweeper is retrofitted into an autonomous sweeper equipped with the Drive-by-Wire (DBW) system, computer, sensors, and actuators essential for autonomous driving. A complete autonomous driving software stack is also developed upon this hardware setup to enable the vehicle to navigate itself safely in various challenging operating environments. the system has been extensively tested in different environments on the National University of Singapore (NUS) campus, including private roads, car parks, warehouses, and public plaza areas.

关键词： Actuators Vacuum systems Full stack Transportation Manuals Cleaning Sensor systems

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Prediction of Emission Characteristics of Spark Ignition (S.I.) Engines with Premium Level Gasoline-Ethanol-Alkane Blends Using Machine Learning 13th

Prediction of Emission Characteristics of Spark Ignition (S....

引用

13th international Advanced Computing conference, IACC 2023

作者： Kumbhar, Sujit Khot, Sanjay Jujare, Varsha Patil, Vishal Husainy, Avesahemad Shedbalkar, Koustubha Department of Technology Shivaji University Maharashtra Kolhapur416004 India Department of Automation and Robotics Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Computer Science and Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Mechanical Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Electrical Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India Department of Mechanical Engineering Sharad Institute of Technology College of Engineering Yadrav Maharastra Inchalkaranji416121 India

ISBN: (纸本)9783031566998

In the current research work, a single cylinder spark (S.I)ignition engine were used for investigations of premium level gasoline-ethanol-alkane experimentally with different operating conditions e.g. variation spark ignition timing. the Engine Lab and PE3 software were used for engine control and data acquisition system. the data obtained after experimentation were used to predict the engine emissions for different operating conditions. the engine emission characteristics were predicted using three machine learning algorithmsviz linear regression, decision tree and random forest. It was found that emissions characteristics such as carbon monoxide, unburnt hydrocarbon found to be minimum for 24°bTDC experimentally as well as predicted by machine learning algorithms with different operating conditions than other spark timing positions such as 15°, 18°, 21°, 27°, 30° bTDC. All three machine learning algorithms gave better results but the random forest algorithm were more accurate than linear regression and decision trees. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Paraffins

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A Real-Time Waste Segregation Using Machine vision for Recycling Facilities

A Real-Time Waste Segregation Using Machine Vision for Recyc...

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China international Electrical and Energy conference (CIEEC)

作者： Muhammad Waqas Qaisar Jingyang Fang Umar Masood School of Control Science and Engineering Shandong University Jinan China School of Electrical Engineering Namal University Mianwali Pakistan

ISBN: (数字)9798350359558

ISBN: (纸本)9798350359565

Recycling facilities are often challenged by the demanding task of waste segregation, a process traditionally requiring substantial time and manpower, and lacking efficient automated solutions. Addressing this, our study explores the use of convolutional neural networks (CNNs), particularly the ResNet-18 model, known for its balance of accuracy and computational efficiency compared to its deeper counterparts like ResNet-50 or ResNet-101. ResNet-18 provides sufficient complexity for precise image classification while enabling faster processing speeds essential for real-time applications. Our method employs this CNN to effectively categorize waste into five distinct types, streamlining the sorting process. In this paper, we introduce a custom-designed hardware system, incorporating motor control for the physical segregation of waste. this system operates in tandem with the ResNet-18's classification results, ensuring that each type of waste is accurately sorted into designated bins. Our integrated approach, which pairs the image classification capabilities of ResNet-18 with a practical hardware automation system, aims to improve the efficiency and accuracy of waste segregation in recycling facilities.

关键词： Waste management Accuracy Machine vision Real-time systems Hardware Recycling Convolutional neural networks

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Evaluation of a novel DSO-based Indoor Ceiling-vision Odometry System

Evaluation of a novel DSO-based Indoor Ceiling-Vision Odomet...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Abdelhak Bougouffa Emmanuel Seignez Samir Bouaziz Florian Gardes Université Paris-Saclay ENS Paris-Saclay CNRS SATIE Gif-sur-Yvette France ez-Wheel La Couronne France

ISBN: (纸本)9781665476881

Indoor localization for mobile industrial robots is a crucial step toward an autonomous system. A mobile robot needs a reliable and robust localization system to achieve its task autonomously. A reasonable estimate of the robot's state can be achieved through Visual Odometry (VO); however, with dynamic objects in the scene, classical VO approaches need to detect and filter these moving objects. Alternatively, we can use an up-facing camera to track the movement with respect to the ceiling, which represents a static and invariant space. this paper presents Ceiling-DSO: an indoor ceiling-vision (CV) system based on Direct Sparse Odometry (DSO). We take advantage of the generic formulation of DSO to avoid making assumptions about the observable shapes or landmarks on the ceiling, making the method generic and applicable to multiple ceiling types. We built a ceiling-vision dataset in a real-world scenario; we then used it to test our approach with different DSO parameters to identify the best fit for robot pose estimation. this paper provides a qualitative and quantitative analysis of the obtained results that showed an acceptable error rate compared to the ground truth.

关键词： Location awareness Visualization Laser radar Service robots Shape Robot vision systems Cameras

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TSN-GReID: Transformer-based Siamese Network for Group Re-Identification

TSN-GReID: Transformer-based Siamese Network for Group Re-Id...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Xiaoyan Lu Weijie Sheng Xinde Li School of Cyber Science and Engineering Southeast University Nanjing China Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing China

ISBN: (纸本)9781665476881

Group re-identification (GReID) is an important yet less-studied task. GReID focuses on associating the group images across non-overlapping cameras. the key challenges of GReID include layout variation, membership changes, and occlusion. Most existing methods focus on the group variation but ignore the occlusion that frequently occurs in the group. In practical scenarios, so many activities like conversing, queuing, or fighting consist of groups. To this end, we design a novel Transformer-based Siamese Network for GReID (TSN-GReID) for joint learning of classification and correspondence tasks to learn more robust group features for group layout and membership changes. Furthermore, we put forward an original regrouping random patch module(RRPM) which respectively regroups the member patch embedding and member-level local features to generate group features with improved discrimination ability and more diversified coverage to deal with occlusion. Experimental results demonstrate the effectiveness of our approach, which significantly outperforms state-of-the-art methods by 4.6 % Rank-1 on the CUHK-SYSU Group (CSG) dataset and by 7.1% Rank-1 on the DukeMTMC Group dataset.

关键词： automation Layout Transformers Cameras Task analysis Robots

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Improving Primal Heuristics for Mixed Integer Programming Problems based on Problem Reduction: A Learning-based Approach

Improving Primal Heuristics for Mixed Integer Programming Pr...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Lingying Huang Xiaomeng Chen Wei Huo Jiazheng Wang Fan Zhang Bo Bai Ling Shi Department of Electronic Engineering Hong Kong University of Science and Technology Kowloon Hong Kong Theory Lab Huawei Hong Kong Research Centre Hong Kong SAR China

ISBN: (纸本)9781665476881

In this paper, we propose a Bi-layer Prediction-based Reduction Branch (BP-RB) framework to speed up the process of finding a high-quality feasible solution for Mixed Integer Programming (MIP) problems. A graph convolutional network (GCN) is employed to predict binary variables' values. After that, a subset of binary variables is fixed to the predicted value by a greedy method conditioned on the predicted probabilities. By exploring the logical consequences, a learning-based problem reduction method is proposed, significantly reducing the variable and constraint sizes. With the reductive sub- MIP problem, the second layer GCN framework is employed to update the prediction for the remaining binary variables' values and to determine the selection of variables which are then used for branching to generate the Branch and Bound (B&B) tree. Numerical examples show that our BP-RB framework speeds up the primal heuristic and finds the feasible solution with high quality.

关键词： Integer programming automation Input variables Time factors Robots

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Engagement Analysis Using DAiSEE Dataset

Engagement Analysis Using DAiSEE Dataset

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international conference on control, automation, robotics and vision (ICARCV)

作者： Naman Solanki Souraj Mandal

ISBN: (纸本)9781665476881

With the world moving towards online communication, the video datastore has exploded in the past few years. Consequently, it has become crucial to analyse participant's engagement level in online communication videos. Engagement prediction of people in videos can be useful in many domains, like education, client meetings, dating, etc. Video-level or frame-level prediction of engagement for a user involves development of robust models that can capture facial micro-emotions efficiently. For the development of an Engagement prediction model, it is necessary to have a widely-accepted standard dataset for engagement analysis. DAiSEE is one of the datasets which consist of in-the-wild data and have a gold standard annotation for engagement prediction. Earlier research done using DAiSEE dataset involved training and testing standard models like CNN based models, but the results were not satisfactory according to industry standards. In this paper, a multi-level classification approach has been introduced to create a more robust model for engagement analysis using DAiSEE dataset. this approach has recorded testing accuracies of 0.638, 0.7728, 0.8195 and 0.866 for predicting boredom level, engagement level, confusion level and frustration level respectively.

关键词： Training Industries Analytical models automation Annotations Computational modeling Predictive models

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3D Object Localization With 2D Object Detector and 2D Localization

3D Object Localization With 2D Object Detector and 2D Locali...

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international conference on control, automation, robotics and vision (ICARCV)

作者： Rafał Staszak Dominik Belter Institute of Robotics and Machine Intelligence Poznan University of Technology Poznań Poland

ISBN: (纸本)9781665476881

In this research, we deal with the problem of estimating 3D object positions based on 2D localization data and 2D object bounding boxes determined on the RGB images by a CNN object detector. We use a mobile-manipulating robot equipped with an RGB-D camera and a 2D laser scanner. In contrast to other methods, which are based either on end-to-end neural networks or machine learning solutions, we propose an approach that allows estimating 3D object positions from a sequence of images and 2D robot poses obtained from an onboard 2D localization system. We determine a set of lines from the robot localization data and the object detections, which define the observation directions. the closest point to a set of lines determines an approximate object location. We define the 3D object position estimation as an optimization problem and solve using efficient GPU implementation. Finally, we present results based on data collected on the real robot in an unstructured environment.

关键词： Location awareness three-dimensional displays Robot vision systems Neural networks Detectors Object detection Machine learning

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