2023 IEEE 21st International Conference on Industrial Informatics (INDIN)最新文献_第7页

Optimization of a High Storage System with two Cranes per Aisle 单通道双吊车高架仓储系统的优化设计

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218152

Niels Grüttemeier, Andreas Bunte, Stefan Windmann

引用次数: 0

Application of the Interoperability Score in the home and building domain 互操作性评分在家庭和建筑领域的应用

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10217895

M. Reinke, E. Root, Christine Rosinger

引用次数: 0

GRAFCET Reduction Techniques for Model Checking 用于模型检查的GRAFCET约简技术

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218247

Robin Mross, Aron Schnakenbeck, Marcus Völker, A. Fay, S. Kowalewski

引用次数: 0

Open Data Platform Tools for Energy Service Ecosystem in Urban Superblocks 城市超级街区能源服务生态系统开放数据平台工具

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10217855

Mikael Filppula, Petri Kannisto, David Hästbacka

引用次数: 0

Experimentation on NN Models for Hazard Identification in Machinery Functional Safety 机械功能安全中危害识别的神经网络模型实验

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218319

Padma Iyenghar, M. Kieviet, Elke Pulvermüller, Juergen Wuebbelmann

引用次数: 0

Network Pruning and Fine-tuning for Few-shot Industrial Image Anomaly Detection 基于网络剪枝和微调的少量工业图像异常检测

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218283

J. Zhang, M. Suganuma, Takayuki Okatani

{"title":"Network Pruning and Fine-tuning for Few-shot Industrial Image Anomaly Detection","authors":"J. Zhang, M. Suganuma, Takayuki Okatani","doi":"10.1109/INDIN51400.2023.10218283","DOIUrl":"https://doi.org/10.1109/INDIN51400.2023.10218283","url":null,"abstract":"This paper focuses on industrial image anomaly detection and localization under few-shot settings. Since acquiring sufficient anomalous data is difficult, unsupervised learning that uses only normal data is commonly used, but even obtaining enough anomaly-free training samples can be challenging. Moreover, applying data augmentations, which is a common strategy for few-shot learning to alleviate the lack of data, is limited to use for some industrial product images. To address the above issues, we propose a network pruning and fine-tuning (PF) framework that leverages the knowledge of a deep pre-trained model. Our approach distills the knowledge of normal samples into a pruned student network, followed by fine-tuning to restore its representation ability for normal data. During inference, discrepancies between features extracted by the teacher and student are used to determine the anomaly score. The proposed method could better utilize the strong representation ability of deep models and benefit the student training with limited data by network pruning. Our framework achieves state-of-the-art performance on the MVTec AD benchmark and is not limited to specific network pruning methods.","PeriodicalId":174443,"journal":{"name":"2023 IEEE 21st International Conference on Industrial Informatics (INDIN)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129700944","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

3D-LiDAR-based Pedestrian Detection for Demand-Oriented Traffic Light Control 基于3d - lidar的行人检测在交通信号灯控制中的应用

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218109

Dennis Sprute, Florian Hufen, Tim Westerhold, Holger Flatt

{"title":"3D-LiDAR-based Pedestrian Detection for Demand-Oriented Traffic Light Control","authors":"Dennis Sprute, Florian Hufen, Tim Westerhold, Holger Flatt","doi":"10.1109/INDIN51400.2023.10218109","DOIUrl":"https://doi.org/10.1109/INDIN51400.2023.10218109","url":null,"abstract":"Traffic lights typically offer push buttons for pedestrians to request crossing the road. Although this method is effective and simple, it has several drawbacks: (1) it requires an explicit user interaction, (2) no information about the number of pedestrians is obtained and (3) there is no information about pedestrians’ vulnerabilities. Thus, it is not possible to optimize the traffic light control in a demand-oriented way. To address this problem, we present a concept for a demand-oriented traffic light control which is based on a novel pedestrian detection and vulnerability classification method. This approach combines privacy-preserving 3D-LiDAR data acquisition and state-of-the-art deep learning methods. An evaluation on real traffic data obtained from two cities in Germany reveals an overall accuracy of 96 % for pedestrian detection and vulnerability classification. Finally, we show how our demand-oriented traffic light control contributes to (1) an automation of pedestrian signal requests, (2) a reduction of pedestrians’ waiting times and (3) an adaption of the green phase’s length according to vulnerabilities.","PeriodicalId":174443,"journal":{"name":"2023 IEEE 21st International Conference on Industrial Informatics (INDIN)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133425551","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

EValueAction: a proposal for policy evaluation in simulation to support interactive imitation learning EValueAction:在模拟中进行政策评估以支持交互式模仿学习的建议

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218251

Fiorella Sibona, Jelle Luijkx, B. Heijden, L. Ferranti, M. Indri

{"title":"EValueAction: a proposal for policy evaluation in simulation to support interactive imitation learning","authors":"Fiorella Sibona, Jelle Luijkx, B. Heijden, L. Ferranti, M. Indri","doi":"10.1109/INDIN51400.2023.10218251","DOIUrl":"https://doi.org/10.1109/INDIN51400.2023.10218251","url":null,"abstract":"The up-and-coming concept of Industry 5.0 fore-sees human-centric flexible production lines, where collaborative robots support human workforce. In order to allow a seamless collaboration between intelligent robots and human workers, designing solutions for non-expert users is crucial. Learning from demonstration emerged as the enabling approach to address such a problem. However, more focus should be put on finding safe solutions which optimize the cost associated with the demonstrations collection process. This paper introduces a preliminary outline of a system, namely EValueAction (EVA), designed to assist the human in the process of collecting interactive demonstrations taking advantage of simulation to safely avoid failures. A policy is pre-trained with human-demonstrations and, where needed, new informative data are interactively gathered and aggregated to iteratively improve the initial policy. A trial case study further reinforces the relevance of the work by demonstrating the crucial role of informative demonstrations for generalization.","PeriodicalId":174443,"journal":{"name":"2023 IEEE 21st International Conference on Industrial Informatics (INDIN)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128565305","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

Systematic Preprocessing of Dielectric Spectroscopy Data and Estimating Viable Cell Densities 介质光谱数据的系统预处理与活细胞密度估算

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218012

Selina Ramm, T. H. Rodríguez, Björn Frahm, M. Pein-Hackelbusch

{"title":"Systematic Preprocessing of Dielectric Spectroscopy Data and Estimating Viable Cell Densities","authors":"Selina Ramm, T. H. Rodríguez, Björn Frahm, M. Pein-Hackelbusch","doi":"10.1109/INDIN51400.2023.10218012","DOIUrl":"https://doi.org/10.1109/INDIN51400.2023.10218012","url":null,"abstract":"For process monitoring, an adequate data preprocessing is crucial to link accessible inline process data with offline measured target variables. Literature, however, does not provide systematic preprocessing strategies. The effects of five different preprocessing strategies on data from a Dielectric Spectroscopy system applied to the Viable Cell Density (VCD) of a mammalian cell cultivation were thus evaluated. Single-frequency measurements are typically used to model the VCD over the growth phase using linear regression or the Cole-Cole model and served as a reference. As multi-frequency measurement is promising to model the VCD beyond the growth phase using Partial Least Squares Regression (PLSR), we further aimed to determine, whether replacing linear regression by PLSR shows comparable modeling performance. All five preprocessing strategies led to comparable results. Exemplary, when using capacitance values at a frequency of 3347 kHz, linear regression resulted in a $mathrm{R}^{2}$ of 0.90 and a standard deviation of 0.4 % on average. Both normalization techniques had the same positive effect on the results of PLSR. The order of smoothing and normalization was irrelevant for both regression methods. Comparing the results of linear regression and PLSR, the latter obtained on average 9 % better results. Therefore, we concluded that PLSR is preferable over linear regression and is potentially suitable to model the VCD beyond the growth phase, which is suggested to be investigated based on more data sets.","PeriodicalId":174443,"journal":{"name":"2023 IEEE 21st International Conference on Industrial Informatics (INDIN)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134589774","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

Entity Component System Architecture for Scalable, Modular, and Power-Efficient IoT-Brokers 面向可扩展、模块化和高能效物联网代理的实体组件系统架构

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218094

Franc Pouhela, Dennis Krummacker, H. Schotten

引用次数: 0