2021 4th International Conference on Artificial Intelligence for Industries (AI4I)最新文献_第2页

Generating Reinforcement Learning Environments for Industrial Communication Protocols 为工业通信协议生成强化学习环境

2021 4th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2021-09-01 DOI: 10.1109/AI4I51902.2021.00022

A. Csiszar, Viktor Krimstein, J. Bogner, A. Verl

引用次数: 1

Calculating the Topological Resilience of Supply Chain Networks Using Quantum Hopfield Neural Networks 利用量子Hopfield神经网络计算供应链网络的拓扑弹性

2021 4th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2021-09-01 DOI: 10.1109/AI4I51902.2021.00023

Nahed Abdelgaber, Chris Nikolopoulos

引用次数: 0

Deep Learning in Remote Sensing: An Application to Detect Snow and Water in Construction Sites 遥感中的深度学习:在建筑工地检测雪和水的应用

2021 4th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2021-09-01 DOI: 10.1109/AI4I51902.2021.00021

Hamidur Rahman, Mobyen Uddin Ahmed, S. Begum, Mats Fridberg, Adam Hoflin

{"title":"Deep Learning in Remote Sensing: An Application to Detect Snow and Water in Construction Sites","authors":"Hamidur Rahman, Mobyen Uddin Ahmed, S. Begum, Mats Fridberg, Adam Hoflin","doi":"10.1109/AI4I51902.2021.00021","DOIUrl":"https://doi.org/10.1109/AI4I51902.2021.00021","url":null,"abstract":"It is important for a construction and property development company to know weather conditions in their daily operation. In this paper, a deep learning-based approach is investigated to detect snow and rain conditions in construction sites using drone imagery. A Convolutional Neural Network (CNN) is developed for the feature extraction and performing classification on those features using machine learning (ML) algorithms. Well-known existing deep learning algorithms AlexNet and VGG16 models are also deployed and tested on the dataset. Results show that smaller CNN architecture with three convolutional layers was sufficient at extracting relevant features to the classification task at hand compared to the larger state-of-the-art architectures. The proposed model reached a top accuracy of 97.3% in binary classification and 96.5% while also taking rain conditions into consideration. It was also found that ML algorithms,i.e., support vector machine (SVM), logistic regression and k-nearest neighbors could be used as classifiers using feature maps extracted from CNNs and a top accuracy of 90% was obtained using SVM algorithms.","PeriodicalId":114373,"journal":{"name":"2021 4th International Conference on Artificial Intelligence for Industries (AI4I)","volume":"94 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114944729","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Planning of Curvature-Optimal Smooth Paths for Industrial Robots Using Neural Networks 基于神经网络的工业机器人曲率最优光滑路径规划

2021 4th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2021-09-01 DOI: 10.1109/AI4I51902.2021.00011

Benjamin Kaiser, A. Verl

{"title":"Planning of Curvature-Optimal Smooth Paths for Industrial Robots Using Neural Networks","authors":"Benjamin Kaiser, A. Verl","doi":"10.1109/AI4I51902.2021.00011","DOIUrl":"https://doi.org/10.1109/AI4I51902.2021.00011","url":null,"abstract":"The use of industrial robots plays an increasingly important role in today’s production technology. Many fabrication processes like milling or gluing have high demands on accuracy and path smoothness to ensure good path tracking. Yet, industrial robot paths in cartesian space mainly consist of a sequence of linear and circular movements and hence show velocity and acceleration discontinuities in the segment transitions. This significantly limits the productivity and machining quality of the robot system. Traversing these discontinuities activates the kinematics mechanically and hence influences the accuracy and surface quality. In addition, the drives of the robot system are subjected to a higher load as a result which decreases the lifetime of the robot. Higher-order continuous paths are required to prevent this. Corner smoothing methods insert smooth, curvature continuous curves in the segment transition resulting in smooth paths. Methods based on polynomial smoothing splines either do not have any curvature-optimality properties or cannot be solved without violating the online constraints of the robot controller. To solve the conflict between the calculation of curvature-optimal smoothing curves and online execution in industrial robot controllers, this paper evaluates the use of neural networks as a model for calculating optimal geometry parameters for corner smoothing with polynomial splines. The model is trained applying supervised learning on a dataset containing offline generated pairs of geometries and optimal parameters. The presented method leads to curvature-optimized smoothing curves and is suitable for online path planning. The application of neural networks to this path planning problem is evaluated in a simulation using a digital twin model of an industrial robot.","PeriodicalId":114373,"journal":{"name":"2021 4th International Conference on Artificial Intelligence for Industries (AI4I)","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124427140","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Enhancing Feature Selection in Single Shot Robot Learning by Using Multi-Modal Inputs 利用多模态输入增强单镜头机器人学习中的特征选择

2021 4th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2021-09-01 DOI: 10.1109/AI4I51902.2021.00010

Christian Groth

引用次数: 0

Structure-borne and Air-borne Sound Data for Condition Monitoring Applications 状态监测应用的结构声和空气声数据

2021 4th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2021-09-01 DOI: 10.1109/AI4I51902.2021.00009

S. Matzka, Johannes Pilz, A. Franke

引用次数: 1

A system to automatically classify LIDAR for use within RF propagation modelling 用于射频传播建模的激光雷达自动分类系统

2021 4th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2021-09-01 DOI: 10.1109/AI4I51902.2021.00019

J. Worsey, I. Hindmarch, S. Armour, D. Bull

引用次数: 0

Efficient Binary Static Code Data Flow Analysis Using Unsupervised Learning 使用无监督学习的高效二进制静态代码数据流分析

2021 4th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2021-09-01 DOI: 10.1109/AI4I51902.2021.00030

James Obert, Timothy Loffredo

引用次数: 0

Explainable Machine Learning to Improve Assembly Line Automation 可解释的机器学习提高装配线自动化

2021 4th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2021-09-01 DOI: 10.1109/AI4I51902.2021.00028

Sharmin Sultana Sheuly, Mobyen Uddin Ahmed, S. Begum, Michael Osbakk

{"title":"Explainable Machine Learning to Improve Assembly Line Automation","authors":"Sharmin Sultana Sheuly, Mobyen Uddin Ahmed, S. Begum, Michael Osbakk","doi":"10.1109/AI4I51902.2021.00028","DOIUrl":"https://doi.org/10.1109/AI4I51902.2021.00028","url":null,"abstract":"Faulty manufactured product causes huge economic loss in the manufacturing industry. A local company produces a power transfer unit (PTU) for the vehicle industry and in this production 3% of PTU are rejected due to a mismatch of shim (a small mechanical part supporting PTU). Today the dimension of a shim is predicted manually by human experts. However, there are several problems due to the manual prediction of shim dimension, automatic central control from the cloud cannot be done. Additionally, it increases rejection rates and as a consequence decreases the reliability of the systems. To solve these problems, in this study shim prediction is implemented in the manufacturing of PTU with explainable Machine Learning (ML) which automates the manual shim selection process in the assembly line and explains the ML prediction. A hybrid approach that combines support vector regression (SVR) and k nearest neighbours (kNN) for the first part of the assembly line and Partial Least Squares (PLS) and kNN for the second part of the assembly line is used for shim prediction. The hybrid approach is selected due to better performance compared to the single ML model approach. Then, the most important features of the hybrid approach were identified with SHAP (SHapley Additive exPlanations). The result indicates due to this improved automation faulty PTU rate decreased from 3% to only 1%. Additionally, it enabled control from the cloud and increased reliability. From the explanation of the hybrid approach, it is evident that one of the features values has more impact on the prediction output and controlling this feature will reduce the rejection rate.","PeriodicalId":114373,"journal":{"name":"2021 4th International Conference on Artificial Intelligence for Industries (AI4I)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116755693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Greedy Heuristic for Cluster Editing with Vertex Splitting 顶点分割聚类编辑的贪婪启发式算法

2021 4th International Conference on Artificial Intelligence for Industries (AI4I) Pub Date : 2021-09-01 DOI: 10.1109/AI4I51902.2021.00017

F. Abu-Khzam, Joseph R. Barr, Amin Fakhereldine, Peter Shaw

引用次数: 4