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2024 IEEE International Conference on Technology Management, Operations and Decisions, ICTMOD 2024

作者： Nazliel, Kerem Kayabay, Kerem Gokalp, Mert Onuralp Aydin, Ebru Gokalp Eren, P. Erhan Kocyigit, Altan Graduate School of Informatics Middle East Technical University Ankara Turkey Converged Computing HLRS University of Stuttgart Stuttgart Germany Data Analytics Center TÜPRAŞ Ankara Turkey Hacettepe University Department of Computer Engineering Ankara Turkey

ISBN: (纸本)9798350367355

The rapid advancements in data Science and Artificial Intelligence require organizations to create strategic roadmaps that align with both technological trends and business objectives. This paper introduces a data-driven approach for developing such strategies, integrating Digital Maturity Assessment, Multivocal Literature Review, Topic Modeling, and SWOT analysis within the data Science Roadmapping (DSR) framework. The proposed approach aims to enhance the accuracy and relevance of roadmap development by combining multiple tools for comprehensive trend identification and organizational capability assessment. Ongoing validation through case studies will assess its practical application and effectiveness. © 2024 IEEE.

关键词： data accuracy

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Reinforcement learning for wind-farm flow control:Current state and future actions

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Theoretical & Applied Mechanics Letters 2023年第6期13卷 455-464页

作者： Mahdi Abkar Navid Zehtabiyan-Rezaie Alexandros Iosifidis Department of Mechanical and Production Engineering Aarhus UniversityAarhus N8200Denmark Department of Electrical and Computer Engineering Aarhus UniversityAarhus N8200Denmark Centre for Digitalization Big Dataand Data AnalyticsAarhus UniversityAarhus N8200Denmark

Wind-farm flow control stands at the forefront of grand challenges in wind-energy *** central issue is that current algorithms are based on simplified models and,thus,fall short of capturing the complex physics of wind farms associated with the high-dimensional nature of turbulence and multiscale wind-farm-atmosphere *** learning(RL),as a subset of machine learning,has demonstrated its effectiveness in solving high-dimensional problems in various domains,and the studies performed in the last decade prove that it can be exploited in the development of the next generation of algorithms for wind-farm flow *** review has two main ***,it aims to provide an up-to-date overview of works focusing on the development of wind-farm flow control schemes utilizing RL *** examining the latest research in this area,the review seeks to offer a comprehensive understanding of the advancements made in wind-farm flow control through the application of RL ***,it aims to shed light on the obstacles that researchers face when implementing wind-farm flow control based on *** highlighting these challenges,the review aims to identify areas requiring further exploration and potential opportunities for future research.

关键词： Wind-farm flow control Turbine wakes Power losses Reinforcement learning Machine learning

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Challenges and Opportunities for Enabling the Next Generation of Cross-Domain dataspaces 2

Challenges and Opportunities for Enabling the Next Generatio...

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2nd International Workshop on Semantics in dataspaces, SDS 2024

作者： Deshmukh, Rohit A. Collarana, Diego Gelhaar, Joshua Theissen-Lipp, Johannes Lange, Christoph Arnold, Benedikt T. Curry, Edward Decker, Stefan Fraunhofer Institute for Applied Information Technology FIT Sankt Augustin Germany RWTH Aachen University Aachen Germany Fraunhofer Institute for Software and Systems Engineering ISST Dortmund Germany Insight Centre for Data Analytics University of Galway Galway Ireland

dataspaces are regarded as a standardized solution for sharing data in a trusted way. However, providing and sharing high-quality data across dataspaces poses several scientific and technical challenges, opening new research avenues. Developing governance models and technologies for supporting cross-domain integration of data and services from the existing single-domain dataspaces represents a significant challenge. In this vision paper, we discuss the challenges for enabling next-generation dataspaces and propose an innovative approach that aims at developing a vision and identifying requirements and building blocks for next-generation dataspaces, followed by defining a roadmap and practical migration paths for the existing dataspaces towards the next-generation dataspaces. © 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).

关键词： Interoperability

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Application of Process Mining to Production Lines Using Industrial Internet of Things 5

Application of Process Mining to Production Lines Using Indu...

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5th IEEE Eurasia Conference on IOT, Communication and engineering, ECICE 2023

作者： Yapakçi, Beyza Akdaǧcik, Zeynep Ayvaz, Bora Ergenç, Ali Fuat Istanbul Technical University Big Data and Business Analytics Prog. Istanbul Turkey Ku Leuven Business Engineering Dept. Leuven Belgium Istanbul Technical University Control and Automation Engineering Dept. Istanbul Turkey

ISBN: (纸本)9798350314694

Process mining has emerged as a powerful solution for analyzing production processes in the manufacturing sector, yet its integration with the Internet of Things (IoT) remains a challenge. Drawing from IoT ontologies and business process context models, we introduced a practical methodology for constructing event logs from industrially gathered IoT data. The study was conducted in a controlled laboratory environment to emulate a discrete production model through an industrial communication and database setup. Employing a systematic process mining framework encompassing data preparation, pre-processing, production process analysis, and interpretation steps, we used industrial controllers and Industrial Internet of Things (IIoT) tools to simulate a sauce production process. The generated data was analyzed using ProM software, fortified by visualization techniques such as the 'log as log skeleton' method, for enhancing process flow comprehension and pinpointing ways for optimization. The study results unveiled a systematic linear structure by employing the Alpha Miner method for process model construction. Notably, the analysis exposes extended duration in the production process, prompting further investigation and refinement. The resultant model provides valuable insights into the actual performance of the process and emphasizes the range of potential process scenarios, making it easier to pinpoint deviations from the ideal model. Through the demonstration of the IIoT data integration, the result significantly contributes to the growing enthusiasm for process-oriented methodologies within the manufacturing sector as well as providing an example approach to transform captured IoT data into event logs via contextualization. Furthermore, the study result provides the groundwork for future research and improvement endeavors to enhance manufacturing practices and outcomes. © 2023 IEEE.

关键词： data analysis Industrial Internet of Things manufacturing process Mining production process

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Driving Behaviour Analysis using Low Sampling GPS Trajectories of Ride-hailing Services 5

Driving Behaviour Analysis using Low Sampling GPS Trajectori...

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5th International Conference on data Science and Information Technology, DSIT 2022

作者： Pakeerathan, Thukaraka Santhirakumar, Thenusan Rajendren, Dilani Shegan Perera, Amal Nguyen, Su University of Moratuwa Department of Computer Science & Engineering Moratuwa Sri Lanka Centre for Data Analytics and Cognition La Trobe University Australia

ISBN: (数字)9781665498685

ISBN: (纸本)9781665498685

There is an abundance of existing systems that focus on driver behaviour analysis. The existing driver behaviour analysis is based on average star ratings, driving quality data, customer complaints, and other feedback. It does not provide a view of where a driver's performance is lacking. The dependability of star ratings may vary due to fake ratings, such as self-rating using several devices and passengers being skewed toward excessively poor or high ratings. Also, this is a biased method because there may be no complaints or feedback for particular drivers. Therefore, to identify the deserving drivers, a realistic picture of how the drivers behave on the road while driving can accurately profile driver performance and safety with the collected GPS data for all individual drivers. © 2022 IEEE.

关键词： Quality control

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An Explainable Artificial Intelligence Approach for Force Estimation from Surface-EMG sing the Element Description Method 32

An Explainable Artificial Intelligence Approach for Force Es...

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32nd IEEE International Symposium on Industrial Electronics, ISIE 2023

作者： Sodenaga, Daiki Takeuchi, Issei Silva, Daswin De Katsura, Seiichiro Keio University Department of System Design Engineering Yokohama Japan Abstech Co. Ltd. Saitama Japan La Trobe University Research Centre for Data Analytics and Cognition Melbourne Australia

ISBN: (纸本)9798350399714

In recent years, the need for technological advancement has been increasing because of the declining birthrate and aging populations. Motion copying is one such technical development that can effectively reproduce human motion. However, the challenges of conventional motion-copying systems include the lack of operability and loss of touch sensation at the abstraction phase. Then, we focused on the force estimation by surface-EMG for abstracting force information. In this paper, we address this challenge by developing an explainable Artificial Intelligence (XAI) approach that can estimate force using surface-EMG and uses element description method to interpret and identify the components contributing towards that estimation. We evaluated the proposed XAI method across several real-world experiments that confirm its value and contribution towards innovations in motion-copying system. © 2023 IEEE.

关键词： Artificial intelligence

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Self-Powered Breath Monitoring Using Electrospun Biodegradable and Biocompatible Textile Sensors

Self-Powered Breath Monitoring Using Electrospun Biodegradab...

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International Workshop on Wearable and Implantable Body Sensor Networks (BSN)

作者： Sanjaya D. G. Karnasooriya Ragalage Hamza Qadeer Garrett B. McGuinness Tomas E Ward Shirley M. Coyle Insight SFI Research Centre for Data Analytics School of Electronic Engineering Dublin City University Dublin Ireland Insight SFI Research Centre for Data Analytics Dublin City University Dublin Ireland School of Mechanical Engineering Dublin City University Dublin Ireland Insight SFI Research Centre for Data Analytics School of Computing Dublin City University Dublin Ireland

ISBN: (数字)9798331530143

ISBN: (纸本)9798331530150

This paper presents the development of self-powered breath monitoring sensors using triboelectric nanogenerators (TENGs) technique fabricated from electro spun polycaprolactone (PCL) and cellulose acetate (CA) fibers. The CA was chemically modified with 0.2M NaOH, achieving a maximum open-circuit voltage (Voc) of 14.3 V, short-circuit current (Isc) of $1.67 \mu \mathrm{A}$ and charge (Qsc) of 6.61 nC. Incorporating 3 % chitosan into PCL further enhanced the sensor's performance, yielding a Voc of 20.8 V, Isc of 3.178 µA, and Qsc of 9.9 nC. The sensor demonstrated excellent sensitivity, with a linear response and a sensitivity of 2.209 V/kPa across a pressure range of 0.625-12.5 kPa. It also exhibited rapid response times of 34–36 ms and recovery times of 24–26 ms. The optimized materials and sensor configuration show promise for continuous real-time breath monitoring. This innovation provides a significant advancement in wearable health monitoring systems, enabling early detection of respiratory illnesses and chronic disease management.

关键词： Chemical sensors Optical fiber sensors Sensitivity Voltage Sensors Time factors Triboelectricity Wearable devices Monitoring Textiles

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Research on Tumor Images Segmentation based on Improved Convolutional Neural Network 24

Research on Tumor Images Segmentation based on Improved Conv...

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5th International Symposium on Artificial Intelligence for Medicine Science, ISAIMS 2024

作者： Wan, Yajie Diao, Su Jiang, Gaozhe Song, Chaoda Department of Computer Science Brown University ProvidenceRI United States Department of Industrial & Systems Engineering Auburn University AuburnAL United States Institute of Operations Research and Analytics National University of Singapore Singapore Singapore School of Engineering Department of Computer and Data Sciences ClevelandOH United States

ISBN: (纸本)9798400717826

Tumors are one of the most common tumors in humans, and the main treatment at present is surgical removal. Preoperative planning of tumors usually requires a professional doctor to manually segment the images, but this process is limited by the doctor’s technical experience, which is time-consuming and labor-intensive. In addition, segmentation is extremely difficult due to the complexity and morphological diversity of tumor lesions. We propose a tumor image segmentation method based on improved Convolutional Neural Network (CNN). The model enhances the feature extraction ability by introducing a deep separable convolutional layer and batch normalization, while using dilated convolution to increase the receptive field, thereby improving the accuracy of segmentation. We also incorporated attention mechanisms into the model to focus more accurately on the tumor area and further optimize the segmentation effect. In order to verify the effectiveness of the model, we conducted experimental analysis on multiple publicly available medical imaging datasets. Experimental results show that the improved convolutional neural network significantly improves the accuracy and efficiency of segmentation compared with traditional methods in tumor image segmentation tasks. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Inductive Relation Prediction with Logical Reasoning Using Contrastive Representations

Inductive Relation Prediction with Logical Reasoning Using C...

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2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

作者： Pan, Yudai Liu, Jun Zhang, Lingling Zhao, Tianzhe Lin, Qika Hu, Xin Wang, Qianying School of Computer Science and Technology Xi'an Jiaotong University China National Engineering Lab for Big Data Analytics China Shaanxi Provincial Key Laboratory of Big Data Knowledge Engineering China Lenovo Research Beijing China

Relation prediction in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant embedding paradigm has a restriction on handling unseen entities during testing. In the real-world scenario, the inductive setting is more common because entities in the training process are finite. Previous methods capture an inductive ability by implicit logic in KGs. However, it would be challenging to preciously acquire entity-independent relational semantics of compositional logic rules and to deal with the deficient supervision of logic caused by the scarcity of relational semantics. To this end, we propose a novel graph convolutional network (GCN)-based model LogCo with logical reasoning by contrastive representations. LogCo firstly extracts enclosing subgraphs and relational paths between two entities to supply the entity-independence. Then a contrastive strategy for relational path instances and the subgraph is proposed for the issue of deficient supervision. The contrastive representations are learned for a joint training regime. Finally, prediction results and logic rules for reasoning are attained. Comprehensive experiments on twelve inductive datasets show that LogCo achieves outstanding performance comparing with SOTA inductive baselines. © 2022 Association for Computational Linguistics.

关键词： Forecasting

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A Novel Ranking Scheme for the Performance Analysis of Stochastic Optimization Algorithms using the Principles of Severity

arXiv

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arXiv 2024年

作者： Chandrasekaran, Sowmya Bartz-Beielstein, Thomas Institute for Data Science Engineering and Analytics TH Köln Steinmüllerallee 1 Gummersbach51643 Germany

Stochastic optimization algorithms have been successfully applied in several domains to find optimal solutions. Because of the ever-growing complexity of the integrated systems, novel stochastic algorithms are being proposed, which makes the task of the performance analysis of the algorithms extremely important. In this paper, we provide a novel ranking scheme to rank the algorithms over multiple single-objective optimization problems. The results of the algorithms are compared using a robust bootstrapping-based hypothesis testing procedure that is based on the principles of severity. Analogous to the football league scoring scheme, we propose pairwise comparison of algorithms as in league competition. Each algorithm accumulates points and a performance metric of how good or bad it performed against other algorithms analogous to goal differences metric in football league scoring system. The goal differences performance metric can not only be used as a tie-breaker but also be used to obtain a quantitative performance of each algorithm. The key novelty of the proposed ranking scheme is that it takes into account the performance of each algorithm considering the magnitude of the achieved performance improvement along with its practical relevance and does not have any distributional assumptions. To demonstrate the advantages of the proposed ranking scheme, we compare the expected run-time metrics of three hyperparameter optimization (HPO) procedures, namely, Irace, a mixed-integer parallel efficient global optimization (MIP-EGO), the mixed-integer evolution strategy (MIES), along with (1+1)EA and grid search(GS) on a genetic algorithm framework for Pseudo-Boolean Optimization (PBO) Suite of 25 problems. The proposed ranking scheme is compared to classical hypothesis testing and the analysis of the results shows that the results are comparable and our proposed ranking showcases many additional benefits. © 2024, CC BY-NC-ND.

关键词： Benchmarking

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