2023 IEEE 21st International Conference on Industrial Informatics (INDIN)最新文献_第9页

Trust Management System for Hybrid Industrial Blockchains 混合工业区块链信任管理系统

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218183

Fatemeh Stodt, C. Reich, A. Sikora, Dominik Welte

引用次数: 0

An adapation Framework for Industry 4.0 Responsive Production Systems 工业4.0响应式生产系统的适应框架

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218127

M. Mabkhot, N. Lohse, P. Ferreira

{"title":"An adapation Framework for Industry 4.0 Responsive Production Systems","authors":"M. Mabkhot, N. Lohse, P. Ferreira","doi":"10.1109/INDIN51400.2023.10218127","DOIUrl":"https://doi.org/10.1109/INDIN51400.2023.10218127","url":null,"abstract":"One of the key aims of Industry 4.0 is to create more responsive systems. Responsiveness and adaptation enable coping with new market requirements or introducing new products, as demonstrated by the challenges posed by COVID-19. Despite the economic and sustainability benefits, practitioners often only consider adapting their systems for limited cases. They rely on simple, intuitive estimates of adaptation metrics and do not optimize these milestone decisions, leading to missed opportunities. Currently, there are no reliable methods for measuring system adaptability, quantifying the effort required to adapt it from one state to another, or optimizing the adaptation decision. This paper proposes a comprehensive adaptation framework based on complexity index quantification, graph network estimation, and multi-objective optimization. The framework outlines three approaches for estimating efforts in adapting the physical system design, upgrading control software, and optimizing the adaptation decision. The application on a lab-sized cell demonstrates the framework’s ability to estimate the adaption processes metric and generate a set of optimized cell states. The application reveals potentials for improvement and extension of the three approaches.","PeriodicalId":174443,"journal":{"name":"2023 IEEE 21st International Conference on Industrial Informatics (INDIN)","volume":"69 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":"124033734","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

Increasing Robustness of Agents’ Decision-Making in Production Automation using Sanctioning 基于制裁的生产自动化智能体决策鲁棒性研究

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10217852

K. Land, L. G. Nardin, Birgit Vogel-Heuser

引用次数: 0

Standards for Information Models Considering Knowledge Distribution in Modular Plants 模块化工厂中考虑知识分布的信息模型标准

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218218

Amy Koch, Nazanin Hamedi, Lukas Furtner, Tobias Kock, Anselm Klose, Jonathan Mädler

引用次数: 0

Designing Redundant Cable-Driven Parallel Robots for Additive Manufacturing using End-Effector Compliance Index 基于末端执行器柔度指数的增材制造冗余索驱动并联机器人设计

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218156

B. Kara, M. S. Qureshi, Zeynep Basaran Bundur, O. Bebek

引用次数: 0

Understanding the Role of Solar PV and Battery Energy Storage in a Snack Bar: A Case Study in Madeira Island 了解太阳能光伏和电池储能在小吃店中的作用:以马德拉岛为例

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218295

Lucas Pereira, Jonathan Cavaleiro, Hugo Morais

引用次数: 0

Individualized Clustered Cooperative Communication Units in Automated Electrical Routing in 3D CAD 三维CAD中自动化电气布线的个性化集群协同通信单元

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218004

Tizian Dagner, Selin Kesler

{"title":"Individualized Clustered Cooperative Communication Units in Automated Electrical Routing in 3D CAD","authors":"Tizian Dagner, Selin Kesler","doi":"10.1109/INDIN51400.2023.10218004","DOIUrl":"https://doi.org/10.1109/INDIN51400.2023.10218004","url":null,"abstract":"Many industries are faced with the dilemma of increasing the amount of wires and cables in their products. The process of mapping the path of these cables is tedious, iterative, and prone to errors. Instead of manually specifying all the waypoints for a diverse set of cables, automation can provide globally optimized and proven pathways accelerating the product development process. To implement automated electrical routing, an industry-appropriate method is seamlessly integrated into existing 3D computer-aided design (CAD) workflows. The aim of this research is to evaluate the effectiveness and practicality of multi-agent reinforcement learning in determining the most efficient paths in three-dimensional space. To achieve this goal, information is extracted directly from 3D CAD and the results are immediately fed back into CAD. This paper proposes a novel approach that involves clustering the cables based on example paths prior to the actual learning process. Then, a communicating multi-agent proximal policy optimization (PPO) algorithm learns the routing process. To solve the shortest path problem in three-dimensional space while considering cable-and environment-specific constraints and minimizing the total cable length, the agents’ accessible space is restricted to a maximum distance from the initial 3D CAD geometry. The developed approach is explained in this paper and compared to established techniques in electrical routing such as the A* algorithm.","PeriodicalId":174443,"journal":{"name":"2023 IEEE 21st International Conference on Industrial Informatics (INDIN)","volume":"11 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":"126831331","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 Standardized Edge Computing Infrastructure of LoRaWAN Using IEEE 2668 基于IEEE 2668的LoRaWAN标准化边缘计算基础架构

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10218036

Zhifu Zhang, Yucheng Liu, G. Hancke, K. Tsang

{"title":"A Standardized Edge Computing Infrastructure of LoRaWAN Using IEEE 2668","authors":"Zhifu Zhang, Yucheng Liu, G. Hancke, K. Tsang","doi":"10.1109/INDIN51400.2023.10218036","DOIUrl":"https://doi.org/10.1109/INDIN51400.2023.10218036","url":null,"abstract":"LoRaWAN is an overwhelmingly popular wide area networking protocol that is capable of deploying city-wide Internet of Things (IoT) networks. Increase on the deployment of LoRaWAN in the last few years has led to exponential increase on the number of LoRa sensory and actuating end devices and results in more congestion in LoRaWAN network traffic. Moreover, the evolving AI-IoT (AIoT) applications (e.g., AI-powered environment monitoring system) create needs for more computing power with low latency requirements. To mitigate the high latency issue and the shortage of computing power due to continuously demanding quality of service (QoS) requirements of IoT applications, the Edge-Cloud server structure has been explored in a thorough manner in research area in recent years. An IEEE 2668 standardized IoT infrastructure system is presented in this work focusing on analyzing the performance of applying Edge-Cloud network server structure to LoRaWAN networks and provide standardized edge computing LoRaWAN infrastructure. This work uses queuing network to quantitively analyze the performance of an edge computing infrastructure applied to LoRaWAN and uses IEEE 2668 to quantitively present the performance and applicability of the Edge-Cloud LoRaWAN infrastructure.","PeriodicalId":174443,"journal":{"name":"2023 IEEE 21st International Conference on Industrial Informatics (INDIN)","volume":"26 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":"125739543","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 multi-output LSTM-CNN learning scheme for power disaggregation within a NILM framework 一种用于NILM框架下功率分解的多输出LSTM-CNN学习方案

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10217936

Yacine Belguermi, P. Wira, Gilles Hermann

{"title":"A multi-output LSTM-CNN learning scheme for power disaggregation within a NILM framework","authors":"Yacine Belguermi, P. Wira, Gilles Hermann","doi":"10.1109/INDIN51400.2023.10217936","DOIUrl":"https://doi.org/10.1109/INDIN51400.2023.10217936","url":null,"abstract":"The non-deterministic home appliances’ behaviour makes aggregated power consumption hard to be explored and to identify individual appliances’ consumption (disaggregation) in residential buildings. This paper presents a deep neural network learning scheme in order to disaggregate a main meter’s aggregated signal into 11 appliances’ signals and estimate their individual power consumption. A 1-Dimensional Convolution Neural Network (1D CNN) and Long Short-Term Memory (LSTM) layers are used together to form a sequence-to-point (S2P) and a sequence-to-sequence (S2S) Multi-Target Regressor (MTR) for learning and recognizing the loads. Our model is fed with the home total real power (P), total reactive power (Q) and total current (I) and outputs the disaggregated real power (P) for each appliance. The model was trained and evaluated on the AMPds2 public dataset which results in a global disaggregation accuracy of 93.27% for the S2P model and 87.79% for the S2S model. The S2P model outperforms the existing methods in terms of disaggregation accuracy and the number of disaggregated appliances (11 appliances instead of 9) on the used database.","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":"131309476","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

Extended Reference Broadcast Infrastructure Synchronization Protocol in 5G and Beyond 5G及以后的扩展参考广播基础设施同步协议

2023 IEEE 21st International Conference on Industrial Informatics (INDIN) Pub Date : 2023-07-18 DOI: 10.1109/INDIN51400.2023.10217973

Michael Gundall, C. Huber, H. Schotten

引用次数: 0