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Proceedings of the Winter Simulation Conference

作者： Alexander Wuttke Markus Rabe Joachim Hunker Jan-Philipp Diepenbrock Dept. IT in Production and Logistics TU Dortmund University Dortmund GERMANY Fraunhofer Institute for Software and Systems Engineering Dortmund GERMANY IVA Schmetz GmbH Menden GERMANY

ISBN: (纸本)9798331534202

Maintenance is pivotal in industry, with condition-based maintenance emerging as a key strategy. This involves monitoring the machine condition through sensor data analysis. Model-based approaches compare observed data with expected values from models, which requires high-quality models. An established method is to use simulation models, which in many cases produce good results but may lack precision due to uncertainties. Alternatively, models created by machine learning can detect patterns directly from data. This paper proposes combining simulation models with machine learning models, leveraging the simulation's a-priori knowledge and machine learning's data patterns to enhance models for condition monitoring. Recurrent neural networks are suggested as the machine learning method. The paper outlines a systematic approach and demonstrates its application in an industrial use case, which investigates vacuum processes in industrial furnaces.

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IoT Based Drip Infusion Monitoring System

IoT Based Drip Infusion Monitoring System

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2022 IEEE Delhi Section Conference, DELCON 2022

作者： Maniktalia, Riddhi Tanwar, Satwik Billa, Ritvik Deepa, K. School of Computer Science and Engineering Vellore Institute of Technology Vellore India School of Computer Science and Engineering Vellore Institute of Technology Department of Software Systems Vellore India

ISBN: (纸本)9781665458832

During recent years, we have seen many technological advancements which help to take better care of patient's health and assure them fast and safe recovery. The most basic item necessary is competent patient care in hospitals, as well as proper management of fluid and electrolytes. In hospital almost all patients, mainly ICU patients, have to regulate the volume of fluids and electrolyte into the bloodstream 247 which is done using drip. These drips need regular monitoring or changing to maintain constant flow of fluids or to prevent any infection to patients. Though this system is simple, overcrowded hospitals and the risk of deficiency of nurses can put patients at health risk which can deteriorate patients' health more aggressively or in worse case it can lead to death. Almost in all hospitals, nurses or the hospital staff are responsible for monitoring the drip level. But unfortunately, because of their hectic schedule, the observer may forget to change the bottle at the appropriate time. During the pandemic, hospitals were overrun with patients, and nurses were unable to do manual regular checks on the drip conditions and drip level of every patient, even after working extra shifts. Many patients even died due to not being able to get proper care from nurses. The next step in providing more effective and easy healthcare is to automate such vital procedures. To overcome this critical situation, we are proposing an IoT (Internet of Things) based Drip Monitor System using Arduino UNO which eases the process of measuring and solves the issues of bubble formation in drips. © 2022 IEEE.

关键词： Health risks

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RobFL: Robust Federated Learning via Feature Center Separation and Malicious Center Detection 40

RobFL: Robust Federated Learning via Feature Center Separati...

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhou, Ting Liu, Ning Song, Bo Lv, Hongtao Guo, Deke Liu, Lei National University of Defense Technology Science and Technology Laboratory on Information Systems Engineering Changsha China School of Software Shandong University Jinan China Shandong Research Institute of Industrial Technology Jinan China

ISBN: (纸本)9798350317152

In recent years, the integration of federated learning and deep learning technologies has become increasingly prevalent in privacy-preserved scenarios, such as smart health applications and automatic financial support. However, the inherent robustness issue in deep learning poses potential risks to federated learning systems when subjected to various attack methods. These attacks can inflict damage during the training and testing phases, perturbing models and inputs. To enhance the robustness of existing federated learning systems, we propose a novel framework called RobFL. This framework incorporates a unique feature learning module -feature center separation learning -that is specifically designed to increase the margins between different classes in the feature space, thereby augmenting the difficulty of attacks employing imperceptible perturbations on inputs. Furthermore, we design a malicious center detection method to detect malicious clients and mitigate their adverse impact. Extensive experiments substantiate the robustness of our proposed framework, RobFL, demonstrating its resilience against both evasion attacks and poisoning attacks. © 2024 IEEE.

关键词： Anomaly detection

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Light field depth estimation:A comprehensive survey from principles to future

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High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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IPL Data Analysis by Web Scraping and Using PowerBI

IPL Data Analysis by Web Scraping and Using PowerBI

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IT Innovation and Knowledge Discovery (ITIKD), International Conference on

作者： Mogith P Mothiswar T B G Mohammad Abubak J. Jagannathan N. Balaganesh Software Engineering Vellore Institute of Technology Vellore India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (数字)9798350355468

ISBN: (纸本)9798350355475

Cricket fans throughout the world avidly follow the Indian Premier League (IPL), a competition of great fame. The IPL data is examined in this using Exploratory Data Analysis (EDA) approaches to find hidden trends, patterns, and insights. Key findings are summarized in EDA, a critical stage of data processing that is frequently complemented with visual aids. Through EDA, the goal is to investigate several factors, including team performance, player data, match results, and venue effects. Data transformation, cleaning, and visualization will all be part of producing insightful findings. Win percentages, player averages, and run distributions are examples of key performance indicators (KPIs) that will be looked at. The analysis's findings will give teams, athletes, and supporters important new information.

关键词： Visualization Technological innovation Fans Data analysis Key performance indicator Data visualization Market research Knowledge discovery Cleaning

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Integrating Scheduling of Logistic Support Processes in Agent-Based Industry 4.0 Assembly Simulation

Integrating Scheduling of Logistic Support Processes in Agen...

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Simulation Winter Conference

作者： Adrian Freiter Christian Schwede Department of Logistics Fraunhofer Institute for Software and Systems Engineering (ISST) Dortmund Germany Faculty of Engineering and Mathematics University of Applied Sciences and Arts Bielefeld Bielefeld Germany

The upcoming decentralized production systems seem to be promising in Industry 4.0 assembly to handle the challenges of highly individual products. Matrix production characterized by freely linked workstations and an advanced automation level are highly flexible. That is why many efforts have already been made to explore the advantages compared to existing flow shop production systems, but also the additional challenges arising from this new paradigm. One of these challenges is the synchronization of main product and supply part flow at the individual workstations during order scheduling. This paper presents a new approach of integrating logistics support processes into the scheduling of the main product flow to consider the part supply in the decisions taken during scheduling avoiding waiting times. We compare our integrated approach with the existing decoupled scheduling approach, based on a "bicycle assembly" scenario. The results are promising particularly when part supply is a bottleneck.

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GAN-Based Data Augmentation for Improving Biometric Authentication Using CWT Images of Blood Flow Sounds

GAN-Based Data Augmentation for Improving Biometric Authenti...

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17th International Joint Conference on Biomedical engineering systems and Technologies, BIOSTEC 2024

作者： Sahare, Natasha Fuentealba, Patricio Salvi, Rutuja Burmann, Anja Henze, Jasmin Fraunhofer Institute for Software and Systems Engineering ISST Dortmund Germany Technical University of Dortmund Dortmund Germany Instituto de Electricidad y Electrónica Facultad de Ciencias de la Ingeniería Universidad Austral de Chile Valdivia Chile IDTM GmbH Recklinghausen Germany

Biometric identification allows to secure sensitive information. Since existing biometric traits, such as finger prings, voice, etc. are associated with different limitations, we exemplified the potential of blood flow sounds for biometric authentication in previous work. Therefore, we used measurements from seven different users acquired with a custom-built auscultation device to calculate the spectrograms of these signals for each cardiac cycle using continuous wavelet transform (CWT). The resulting spectral images were then used for training of a convolutional neural network (CNN). In this work, we repeated the same experiment with data from twelve users by adding more data from the original seven users and data from five more users. This lead to an imbalanced dataset, where the amount of available data for the new users was much smaller, e.g., U1 had more than 900 samples per side whereas the new user U9 had less than 100 samples per side. We experienced a lower performance for the new users, i.e. their sensitivity was 18-21% lower than the overall accuracy. Thus, we examined whether the augmentation of data leads to better results. This analysis was performed using generative adversarial networks (GANs). The newly generated data was then used for training of a CNN with several different settings, revealing the potential of GAN-based data augmentation for increasing the accuracy of biometric authentication using blood flow sounds. © 2024 by SCITEPRESS – Science and Technology Publications, Lda.

关键词： Generative adversarial networks

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Concept About Shared Digital Twin of EV Batteries to Improve the Data Exchange in the Context of Battery Transport

Concept About Shared Digital Twin of EV Batteries to Improve...

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IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI

作者： Max Plotnikov Arkadius Schier Software & Information Engineering Fraunhofer Institute for Material Flow & Logistics Dortmund Germany

The present work is based on a concept to improve the information exchange during off-site transport of batteries in the context of electromobility with different actors of the value chain using descriptive research design. The challenges regarding the exchange of information with corresponding actors such as the responsible authorities or safety institutions form the main research subject of this thesis. A measure of the European Commission for the electronic provision of transport information combined with the Shared Digital Twin of a battery by means of Battery Passport makes it possible to improve the error-prone current process, especially in the B2A sector. Hereby, synergy effects arise for different actors in terms of data availability, - integrity and - sovereignty through cloud-based provision of the transport information of batteries.

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Deep neural network applications on pose estimation and action recognition for precision dairy farming 10

Deep neural network applications on pose estimation and acti...

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10th European Conference on Precision Livestock Farming, ECPLF 2022

作者： Rejas-Haddioui, D. Purcell, W. Neubauer, T. Information and Software Engineering Group Institute of Information Systems Engineering Faculty of Informatics TU Wien Favoritenstrasse 9-11/194 Vienna1040 Austria

ISBN: (纸本)9788396536006

Recent advances in Computer Vision (CV) have yielded great improvements in tasks such as Pose Estimation (PE) and Activity Recognition (AR) and their application to the field of Precision Livestock Farming (PLF) have the potential to enable truly non-intrusive animal monitoring. These systems can help detect health issues by providing detailed behaviour analysis and automatically detect problematic conditions like lameness. Past solutions rely on RFID chips and inertial measurement unit (IMU) to identify and classify cattle behaviour, while existing research of CV solutions for animal PE or AR often focus on scenes with single animals and in very clear conditions. Our work aims to study the capabilities and limitations of Computer Vision systems applied under industrial conditions. We train a Deep Neural Network (DNN) system that can predict the pose of each animal in the image, and to also predict the activity they are performing. Furthermore, we explore the deployment capabilities of these systems in industrial settings by studying the effect of neural network pruning in the inference accuracy and cost of the system in an effort to help future solutions be light-weight and with affordable hardware requirements. © ECPLF 2022. All rights reserved.

关键词： Computer vision

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Street Light Cum Garbage System Optimal Design Based on IOT 2

Street Light Cum Garbage System Optimal Design Based on IOT

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2nd International Conference on Emerging Trends in Information Technology and engineering, ic-ETITE 2024

作者： Rajesh, G. Deepa, J. Ezilarasan, M.R. Syed Fiaz, A. Siva Rama Lingham, N. Jagannathan, J. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Information Technology Chennai India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Department of Software and Systems Engineering Vellore India

ISBN: (纸本)9798350328202

Street lights currently use more energy than other types of lighting because of an inefficient mechanism that makes the bulbs use a lot of electricity. The suggested approach uses various sensors on intelligent streetlights to keep an eye on and manage the lights. It has sensors for measuring temperature, brightness, and power that control dimming levels and keep track of status. One Zigbee network serves as the connection point for these lights. In the modification, a cloud- and IoT-based installation was made. Through Zigbee -based data transmission, sensor values are recorded on a distant server known as the Cloud. Additionally, it lowers electricity theft. We include a trash can notice in addition to this street light idea. When the garbage can is whole, a machine automatically notifies the business as people approach the sun, and the brightness of the street lights changes or is increased. © 2024 IEEE.

关键词： Light emitting diodes

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