International Conference on Innovative Intelligent Industrial Production and Logistics最新文献_第8页

TMRobot Series Toolbox: Interfacing Collaborative Robots with MATLAB TMRobot系列工具箱:用MATLAB连接协作机器人

International Conference on Innovative Intelligent Industrial Production and Logistics Pub Date : 1900-01-01 DOI: 10.5220/0011526000003329

João Pereira, Mauro Queirós, Nuno M. C. da Costa, S. Marcelino, José Meireles, Jaime C. Fonseca, António Moreira, João Borges

引用次数: 1

Characterizing N-Dimension Data Clusters: A Density-based Metric for Compactness and Homogeneity Evaluation 表征n维数据簇:紧凑性和同质性评估的基于密度的度量

International Conference on Innovative Intelligent Industrial Production and Logistics Pub Date : 1900-01-01 DOI: 10.5220/0010657500003062

Dylan Molinié, K. Madani

{"title":"Characterizing N-Dimension Data Clusters: A Density-based Metric for Compactness and Homogeneity Evaluation","authors":"Dylan Molinié, K. Madani","doi":"10.5220/0010657500003062","DOIUrl":"https://doi.org/10.5220/0010657500003062","url":null,"abstract":": The new challenges Science is facing nowadays are legion; they mostly focus on high level technology, and more speciﬁcally Robotics , Internet of Things , Smart Automation (cities, houses, plants, buildings, etc.), and more recently Cyber-Physical Systems and Industry 4.0 . For a long time, cognitive systems have been seen as a mere dream only worth of Science Fiction. Even though there is much to be done, the researches and progress made in Artiﬁcial Intelligence have let cognition-based systems make a great leap forward, which is now an actual great area of interest for many scientists and industrialists. Nonetheless, there are two main obstacles to system’s smartness: computational limitations and the inﬁnite number of states to deﬁne; Machine Learning-based algorithms are perfectly suitable to Cognition and Automation, for they allow an automatic – and accurate – identiﬁcation of the systems, usable as knowledge for later regulation. In this paper, we discuss the beneﬁts of Machine Learning, and we present some new avenues of reﬂection for automatic behavior correctness identiﬁcation through space partitioning, and density conceptualization and computation.","PeriodicalId":380008,"journal":{"name":"International Conference on Innovative Intelligent Industrial Production and Logistics","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132958423","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

Edge Containerized Architecture for Manufacturing Process Time Series Data Monitoring and Visualization 制造过程时间序列数据监控与可视化的边缘容器化体系结构

International Conference on Innovative Intelligent Industrial Production and Logistics Pub Date : 1900-01-01 DOI: 10.5220/0011574500003329

Ander García, Xabier Oregui, Javier Franco, Unai Arrieta

引用次数: 0

Human-Robot Collaboration (HRC) with Vision Inspection for PCB Assembly 人机协作与PCB装配视觉检测

International Conference on Innovative Intelligent Industrial Production and Logistics Pub Date : 1900-01-01 DOI: 10.5220/0011526200003329

Mauro Queirós, João Pereira, Nuno M. C. da Costa, S. Marcelino, José Meireles, Jaime C. Fonseca, António Moreira, João Borges

引用次数: 0

Root Cause Classification of Temperature-related Failure Modes in a Hot Strip Mill 热连轧机温度相关失效模式的根本原因分类

International Conference on Innovative Intelligent Industrial Production and Logistics Pub Date : 1900-01-01 DOI: 10.5220/0011380300003329

S. Latham, C. Giannetti

引用次数: 2

Infrastructure for an Integrated Industry 4.0 Life Cycle Spanning Design and Production Platform 集成工业4.0生命周期跨越设计和生产平台的基础设施

International Conference on Innovative Intelligent Industrial Production and Logistics Pub Date : 1900-01-01 DOI: 10.5220/0010055400690075

R. Mühlbacher, Hans Göpfrich, A. Gàlffy

引用次数: 0

Automatic Feature Extraction for Bearings' Degradation Assessment using Minimally Pre-processed Time series and Multi-modal Feature Learning 基于最小预处理时间序列和多模态特征学习的轴承退化评估自动特征提取

International Conference on Innovative Intelligent Industrial Production and Logistics Pub Date : 1900-01-01 DOI: 10.5220/0011548000003329

A. L. Alfeo, M. G. Cimino, G. Gagliardi

{"title":"Automatic Feature Extraction for Bearings' Degradation Assessment using Minimally Pre-processed Time series and Multi-modal Feature Learning","authors":"A. L. Alfeo, M. G. Cimino, G. Gagliardi","doi":"10.5220/0011548000003329","DOIUrl":"https://doi.org/10.5220/0011548000003329","url":null,"abstract":": Maintenance activities can be better planned by employing machine learning technologies to monitor an asset’s health conditions. However, the variety of observable measures (e.g. temperature, vibration) and behaviours characterizing the health degradation process results in time-consuming manual feature extraction to ensure accurate degradation stage recognitions. Indeed, approaches able to provide automatic feature extraction from multiple and heterogeneous sources are more and more required in the field of predictive maintenance. This is-sue can be addressed in a data-driven fashion by using feature learning technology, enabling the transformation of minimally processed time series into informative features. Given its capability of discovering meaningful patterns in data while enabling data fusion, many feature learning approaches are based on deep learning technology (e.g. autoencoders). In this work, an architecture based on autoencoders is used to automatically extract degradation-representative features from minimally preprocessed time series of vibration and temperature data. Different autoencoder architectures are implemented to compare different data fusion strategies. The proposed approach is tested considering both the recognition performances and the quality of the learned features with a publicly available real-world dataset about bearings’ progressive degradation. The proposed approach is also compared against manual feature extraction and the state-of-the-art technology in feature learning.","PeriodicalId":380008,"journal":{"name":"International Conference on Innovative Intelligent Industrial Production and Logistics","volume":"201 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122568156","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

New Benchmarks and Optimization Model for the Storage Location Assignment Problem 存储位置分配问题的新基准和优化模型

International Conference on Innovative Intelligent Industrial Production and Logistics Pub Date : 1900-01-01 DOI: 10.5220/0011378400003329

Johan Oxenstierna, J. Malec, Volker Krüger

引用次数: 0

A Family of Digital T Workflows and Architectures: Exploring Two Cases 一系列数字T工作流程和架构:探索两个案例

International Conference on Innovative Intelligent Industrial Production and Logistics Pub Date : 1900-01-01 DOI: 10.1007/978-3-031-37228-5_6

Randy Paredis, Cláudio Gomes, H. Vangheluwe

引用次数: 0

An Online Variant of Set Cover Inspired by Happy Clients 集封面的一个在线变体的灵感来自快乐的客户

International Conference on Innovative Intelligent Industrial Production and Logistics Pub Date : 1900-01-01 DOI: 10.1007/978-3-031-37228-5_8

Christine Markarian

引用次数: 0