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A Method of Emergency Volunteer Team Internal Participation in Rescue Decision-Making Considering Psychological Behavior

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Journal of Systems Science and Information 2024年第1期12卷 64-80页

作者： Jie BAI Shengqun CHEN College of Computer and Information Sciences Fujian Agriculture and Forestry UniversityFuzhou 350002China School of Information Engineering Fujian Business UniversityFuzhou 350506China Engineering Research Center of Big Data Analytics for Business Intelligence Fujian Province UniversityFuzhou 350506China

A method for internal participation in rescue decision-making of emergency volunteer teams considering psychological behavior is proposed to address the time sequence of rescue ***,the problem of multi-tasking and multi-operation within the emergency volunteer team is ***,considering that task leaders are influenced by behavioral and psychological factors in the evaluation,the required time for the job is used as a reference point,and the expected time that volunteers can complete the job is used as an attribute *** task leader's prospect satisfaction value for each volunteer is calculated based on prospect theory,and the perceived utility values of disappointment theory and regret theory are calculated to measure the task leader's satisfaction with each ***,a multilayer coded genetic algorithm is used to construct an optimization model for emergency volunteer decision-making with the objective of maximizing the satisfaction ***,the feasibility and effectiveness of this method are illustrated by an example *** result shows that the efficiency of rescue tasks can be improved through decision optimization within the volunteer team.

关键词： emergency volunteers rescue task multi-layer coding genetic algorithm satisfaction value psychological behavior

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AI4RFQ: Exploiting User Annotations towards Automating the Extraction of Information and Requirements from Specification Documents

AI4RFQ: Exploiting User Annotations towards Automating the E...

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2021 Workshop on Automation Experience at the Workplace, AutomationXP 2021

作者： Kaiser, Rene Stern, Hermann Know-Center-Research Center for Data-Driven Business and Big Data Analytics Austria

A common task in business-to-business relationships is the analysis of specification documents within a request for quotation (RFQ) process. For non-standard products or services, one company with a need provides detailed specifications to potential suppliers and invites them to make an offer. In order to create an offer that fits with the specification, a supplier company needs to carefully inspect the documents to extract any requirements that are relevant for designing the contents of this offer and calculating its price. In a research effort focusing on HCI and natural language processing aspects, we seek methods and technology to support such RFQ processes. We have created a software tool that supports the reading and requirements identification process with a set of intelligent features that are expected to speed up the process significantly. Whenever users are extracting information, they are asked to annotate all text passages that contain relevant information. This annotation data is used to train machine learning models that provide suggestions in the future. The degree of automation increases continuously as more example data is collected. We strive to participate in the 'Automation Experience at the Workplace' workshop to discuss this change in work practice for our target users in terms of a technical as well as a user experience dimension. We would like to especially reflect on users' motivation to cooperate with the system and provide useful annotations (sharing their precious work expertise), the question of responsibility when dealing with automatic suggestions, as well as the fear of being replaced by 'an Artificial Intelligence' in the near future. Copyright © 2021 for this paper by its authors.

关键词： Automation

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Volunteer rescue dispatch during the Coronavirus Disease 2019 epidemic:Using the recruitment of volunteers for pneumonia epidemic prevention and control in Chun’an County as an example

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Journal of Safety Science and Resilience 2022年第4期3卷 330-339页

作者： Liting Chen Shengqun Chen Jing Zheng Jianqing Gao College of Electronics and Information Science Fujian Jiangxia UniversityFuzhou 350108China Research Institute for Data Analysis and Intelligent Decision-Making Fujian Jiangxia UniversityFuzhou 350108China School of Information Engineering Fujian Business UniversityFuzhou 350012China Fujian Provincial Universities Engineering Research Center of Big Data Analytics for Business Intelligence Fujian Business UniversityFuzhou 350012China

To solve the problem of volunteer dispatch during the Coronavirus Disease 2019(COVID-19)epidemic,a many-to-many two-sided matching volunteer dispatch method based on an improved predator-search algorithm is ***,different evaluation index sets for volunteers and rescue tasks were developed,and weightings were determined using the analytic hierarchy ***,the actual and expected values of the different indicators of the two parties were determined,and the triangular fuzzy number was used to calculate the satis-faction of the two *** on this number,we used a linear weighting method to calculate the combined satisfaction and build a many-to-many two-sided matching model according to the demands of both ***-sequently,an improved predator-search algorithm was used to solve the ***,taking the recruitment of volunteers for pneumonia epidemic prevention and control in Chun’an County as an example,the method proposed in our study was verified.A comparison and analysis of the results further demonstrated the feasibility and advantages of this method.

关键词： Volunteer Many-to-many Two-sided matching Predator-search algorithm

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Deploying DAPHNE Computational Intelligence on EuroHPC Vega for Benchmarking Randomised Optimisation Algorithms

Deploying DAPHNE Computational Intelligence on EuroHPC Vega ...

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International Conference on Broadband Communications for Next Generation Networks and Multimedia Applications (CoBCom)

作者： Aleš Zamuda Mark Dokter Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia KNOW-CENTER GmbH Research Center for Data-Driven Business & Big Data Analytics Graz Austria

ISBN: (数字)9798350388459

ISBN: (纸本)9798350388466

This paper presents a deployment of DAPHNE (Integrated data Analysis Pipelines for Large-Scale data Management, HPC and Machine Learning) for Computational Intelligence (CI). As CI progresses, e.g. through effort in human societies like the IEEE Computational Intelligence Society (CIS), the latest advances have become more widely popularized. Likewise, with the accessibility of supercomputing resources to advance CI, like with infrastructures such as EuroHPC machines including Vega, this paper demonstrates deployment of Randomised Optimisation Algorithms (ROAs) as a subset of CI algorithms, on EuroHPC Vega. As the Vega is a cluster of distributed nodes, a successful computation on several nodes at once in parallel and concurrently, requires an orchestrated deployment across the nodes, batched using Slurm in this paper. An example ROA benchmarking scenario is then presented in this scope.

关键词： data analysis Pipelines Clustering algorithms Text summarization Machine learning Benchmark testing Timing

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Application of an Improved Convolutional Neural Network-Based Method in Network Intrusion Detection 3

Application of an Improved Convolutional Neural Network-Base...

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3rd International Conference on Intelligent Communications and Computing, ICC 2023

作者： Chen, Huan You, Guirong Shiue, Yeouren College of Information Engineering Fujian Business University Fuzhou China Fujian Prov. Universities Eng. Research Center of Big Data Analytics for Business Intelligence Fujian Business University Fuzhou China National Tsing Hua University Department of Industrial Engineering and Engineering Management Hsinchu Taiwan

ISBN: (纸本)9798350308327

This study investigates the use of convolutional neural network (CNN) technology to solve the problem of network intrusion detection, specifically studying the impact of convolutional layer count. Experiments revealed that two convolutional layers yield optimal results. An AdaBoost+CNN model was proposed using this CNN with optimization strategies such as ensemble learning. Eight classical learning algorithms were compared with the proposed methods, and the results were evaluated using parameters such as accuracy, precision, and F-measurement. Compared with traditional machine learning, deep learning, and other methods, the method proposed in this study achieved higher classification accuracy and successful results in detecting network attacks. © 2023 IEEE.

关键词： Adaptive boosting

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Live Video Assistance Systems for Assembly Processes 9th

Live Video Assistance Systems for Assembly Processes

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9th IFIP International Precision Assembly Seminar, IPAS 2020

作者： Kittl, Christian Streibl, Markus Evolaris Next Level GmbH Graz Austria Know-Center GmbH Research Center for Data-Driven Business and Big Data Analytics Graz Austria

ISBN: (纸本)9783030726317

Work processes and assembly processes are increasingly gaining in complexity in the industrial context and demand a wealth of knowledge from assembly employees, as well as from service and maintenance personnel. The article describes a system developed in order to support assembly workers using a live video assistance system in combination with "wearables" - in particular smart glasses - in complex assembly processes by experts and reports findings from an acceptance analysis study. © 2021, The Author(s).

关键词： Assembly

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APPROXIMATING FAMILIES OF SHARP SOLUTIONS TO FISHER'S EQUATION WITH PHYSICS-INFORMED NEURAL NETWORKS

arXiv

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arXiv 2024年

作者： Rohrhofer, Franz M. Posch, Stefan Gößnitzer, Clemens Geiger, Bernhard C. Know-Center GmbH Research Center for Data-Driven Business & Big Data Analytics Sandgasse 36/4 Graz8010 Austria LEC GmbH Research Center for Large Engine Technology Inffeldgasse 19 Graz8010 Austria

This paper employs physics-informed neural networks (PINNs) to solve Fisher's equation, a fundamental reaction-diffusion system with both simplicity and significance. The focus is on investigating Fisher's equation under conditions of large reaction rate coefficients, where solutions exhibit steep traveling waves that often present challenges for traditional numerical methods. To address these challenges, a residual weighting scheme is introduced in the network training to mitigate the difficulties associated with standard PINN approaches. Additionally, a specialized network architecture designed to capture traveling wave solutions is explored. The paper also assesses the ability of PINNs to approximate a family of solutions by generalizing across multiple reaction rate coefficients. The proposed method demonstrates high effectiveness in solving Fisher's equation with large reaction rate coefficients and shows promise for meshfree solutions of generalized reaction-diffusion systems. © 2024, CC BY-NC-ND.

关键词： Reaction rates

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BOAT: A Bayesian Optimization AutoML Time-series Framework for Industrial Applications 7

BOAT: A Bayesian Optimization AutoML Time-series Framework f...

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7th IEEE International Conference on big data Computing Service and Applications, bigdataService 2021

作者： Kurian, John Joy Dix, Marcel Amihai, Ido Ceusters, Glenn Prabhune, Ajinkya Industrial Data Analytics ABB Corporate Research Center Ladenburg Germany Energy Industries ABB Process Automation Brussels Belgium Big Data and Business Analytics SRH University Heidelberg Germany

ISBN: (纸本)9781665434836

Driven by the increasing degree of automation, industrial production plants have become very data reliant, which poses a great potential for machine learning applications. AutoML is a fledgling research topic that has lately gained much attention in the industrial domain. However, existing applications of AutoML are limited, as industrial systems typically involve time-series data, while AutoML solutions for this type of data seem to be still underrepresented. On the contrary, existing AutoML libraries provide better solutions for, e.g., image, textual, tabular or categorical data. To close this gap to the data types and requirements that are typically found in the industrial domain, especially w.r.t. time-series data, a reusable framework is presented that provides native support for time-series models. The framework is equipped with 1) optimization support for a large number of model and hyperparameter configurations, 2) a warm starting module that performs meta-learning, 3) native support for time-series models, 4) an API for enabling user-defined custom models, and 5) a User Interface that provides a holistic view of the optimization results and deployment instructions. Experimental results show the framework's competitive performance on time-series data and the effectiveness of the warm starting module in accelerating the optimization procedure. A qualitative analysis of the API is done to showcase the framework's usability regarding defining custom models. © 2021 IEEE.

关键词： User interfaces

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On the Role of Fixed Points of Dynamical Systems in Training Physics-Informed Neural Networks

arXiv

引用

arXiv 2022年

作者： Rohrhofer, Franz M. Posch, Stefan Gößnitzer, Clemens Geiger, Bernhard C. Know-Center GmbH Research Center for Data-Driven Business & Big Data Analytics Sandgasse 36/4 Graz8010 Austria LEC GmbH Large Engines Competence Center Inffeldgasse 19 Graz8010 Austria

This paper empirically studies commonly observed training difficulties of Physics-Informed Neural Networks (PINNs) on dynamical systems. Our results indicate that fixed points which are inherent to these systems play a key role in the optimization of the in PINNs embedded physics loss function. We observe that the loss landscape exhibits local optima that are shaped by the presence of fixed points. We find that these local optima contribute to the complexity of the physics loss optimization which can explain common training difficulties and resulting nonphysical predictions. Under certain settings, e.g., initial conditions close to fixed points or long simulations times, we show that those optima can even become better than that of the desired solution. Copyright © 2022, The Authors. All rights reserved.

关键词： Dynamical systems

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Bringing Chemistry to Scale: Loss Weight Adjustment for Multivariate Regression in Deep Learning of Thermochemical Processes

arXiv

引用

arXiv 2023年

作者： Rohrhofer, Franz M. Posch, Stefan Gößnitzer, Clemens García-Oliver, José M. Geiger, Bernhard C. Know-Center GmbH Research Center for Data-Driven Business & Big Data Analytics Sandgasse 36/4 Graz8010 Austria LEC GmbH Large Engines Competence Center Inffeldgasse 19 Graz8010 Austria CMT Motores Térmicos Universitat Politècnica de València Camí de Vera València46022 Spain

Flamelet models are widely used in computational fluid dynamics to simulate thermochemical processes in turbulent combustion. These models typically employ memory-expensive lookup tables that are predetermined and represent the combustion process to be simulated. Artificial neural networks (ANNs) offer a deep learning approach that can store this tabular data using a small number of network weights, potentially reducing the memory demands of complex simulations by orders of magnitude. However, ANNs with standard training losses often struggle with underrepresented targets in multivariate regression tasks, e.g., when learning minor species mass fractions as part of lookup tables. This paper seeks to improve the accuracy of an ANN when learning multiple species mass fractions of a hydrogen (H2) combustion lookup table. We assess a simple, yet effective loss weight adjustment that outperforms the standard mean-squared error optimization and enables accurate learning of all species mass fractions, even of minor species where the standard optimization completely fails. Furthermore, we find that the loss weight adjustment leads to more balanced gradients in the network training, which explains its effectiveness. © 2023, CC BY.

关键词： Table lookup

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