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13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019

作者： Furuta, Hitoshi Ishibashi, Ken Nakatsu, Koichiro Faculty of Informatics Kansai University Osaka Japan Data Science Laboratory Kansai University Osaka Japan Department of Modern Life Osaka Jonan Women's Junior College Osaka Japan

The purpose of this study is to enhance the resilience of restoration scheduling for road networks. Optimal scheduling for resource allocation plays an important role on early restoration. However, circumstances in the post-seismic period are more likely to change due to various factors such as secondary disaster. Furthermore, it is necessary to prepare for unexpected damages. This study examines a method to reduce changes of restoration plan with keeping the adaptability against uncertainties in the post-seismic period. Specifically, an attempt is made to clarify factors strongly related to the delay of restoration in the flexible scheduling which is based on allocation priorities of restoration groups proposed by the existing studies. In this study, numerical simulations are presented to investigate the influence of the change of schedule on early restoration. And then, this paper discussed a method to keep initial restoration plan and to enhance adaptability to circumstances' changes. © 13th International Conference on Applications of Statistics and Probability in Civil Engineering, ICASP 2019. All rights reserved.

关键词： Restoration

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Demand Analysis of a German Emergency Medical Service Feedback System

Demand Analysis of a German Emergency Medical Service Feedba...

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68th Annual Meeting of the German Association of Medical informatics, Biometry, and Epidemiology: science. Close to People, GMDS 2023

作者： Keimer, Marina Villis, Marten Christoph, Jan Rödle, Wolfgang Department of Medical Informatics Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany medical Data Science Faculty of Medicine Martin-Luther-University Halle-Wittenberg Halle Germany

ISBN: (纸本)9781643684284

Background: The number of emergency medical service (EMS) calls in Germany is continuously increasing. The initial assessment, the pre-hospital care and the choice of hospital for further care by the EMS influences the patient's outcome and are the basis for further care in hospital. However, the EMS does not receive any official feedback on its decisions. Objectives: This study evaluates the demand for a feedback system from the emergency department (ED) to the EMS, what it should contain, and how it could be integrated in the electronic clinical systems. Methods: A semi-structured interview guideline for expert interviews with members of EMS staff (n = 6) and ED staff (n = 17) was developed. A mockup to visualise a possible implementation was designed and included in the interview. Results: There is a significant demand for feedback on pre-diagnosis, pre-hospital care and handover of patients from the EMS to the ED. The EDs are very interested in improving the collaboration with the paramedic services through feedback. Conclusion: A feedback system is strongly desired by various EMS stakeholders and, according to them, could improve both EMS and ED collaboration and overall patient care. © 2023 The authors and IOS Press.

关键词： Diagnosis

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Assessing Conventional and Deep Learning-Based Approaches for Named Entity Recognition in Unstructured Hungarian Medical Reports

Assessing Conventional and Deep Learning-Based Approaches fo...

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International Symposium on Applied Machine Intelligence and informatics (SAMI)

作者： Gergő Bogacsovics Balázs Harangi Marcell Beregi-Kovács Dávid Kupás Róbert Lakatos Norbert Dániel Serbán Attila Tiba János Tóth Department of Data Science and Visualization Faculty of Informatics University of Debrecen Debrecen Hungary

In digital healthcare, much patient data is available in text format. The structuring of this data, according to standards, has yet to be widely used, including in Hungary, where it is available in unstructured form. To make these patient records easy to filter and search, they must be processed and structured. Using modern natural language processing and deep learning techniques has resulted in effective systems for implementing such workflows. However, selecting appropriate algorithms for specific text-processing tasks is still a challenging issue. This is due to the scarcity of benchmarks and the variety of architectures available. This article evaluates models for named entity recognition (NER) in digital medical reports written in Hungarian. We evaluate traditional, recurrent neural network, and transformer-based approaches for NER using a dataset comprising 801 positron emission tomography scans and annotated medical reports. The medical reports were annotated to cover six different entity classes and reviewed by clinical experts to ensure accuracy. We present a comprehensive assessment of various methods and provide insight into addressing NER problems in the case of low-resource languages such as Hungarian.

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Using Arduino Iot Modules As A Low Cost Environmental Research Monitoring System 1

Using Arduino Iot Modules As A Low Cost Environmental Resear...

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1st International Conference on Computer science and Emerging Technologies, CSET 2023

作者： Magableh, Murad Marie, Ziyad Mohamed, Rajina R. Bin Ibrahim, Mohd Hafizuddin Jusoh, Julaily Aida Kumar, Rohit Irbid National University Faculty of Business and Financial Science MIS BI and Data Analysis Department Irbid Jordan Al-Istiqlal University Faculty of Administrative Sciences and Informatics Public Administration Department Palestine College of Computing Dan Informatics Universiti Tenaga Nasional Malaysia Politeknik Kuala Terenngganu Department of Electrical Engineering Kuala Terengganu20200 Malaysia UniSZA Faculty of Informatics and Computing Malaysia School of Computer Science and Engineering Jain Deemed to Be University Bangalore India

ISBN: (纸本)9798350341737

Environmental monitoring plays a crucial role in understanding the impact of human activities on our ecosystems. However, the cost and complexity of traditional monitoring systems often hinder widespread deployment, especially in resource-constrained regions. This research paper explores the feasibility and effectiveness of employing Arduino Internet of Things (IoT) modules as a low-cost alternative for environmental research monitoring. The study focuses on designing and implementing an Arduino-based environmental monitoring system capable of gathering real-time data on key environmental parameters, such as temperature, humidity, air quality, and soil moisture. The hardware setup comprises Arduino microcontrollers interfaced with a range of environmental sensors and connectivity modules for data transmission. To assess the system's reliability and accuracy, a series of field experiments were conducted in diverse environmental settings, including urban areas, agricultural zones, and natural habitats. The collected data was compared against measurements from conventional monitoring equipment to validate the Arduino-based system's performance. The research demonstrates that the Arduino IoT modules can effectively monitor and transmit environmental data with a reasonable level of accuracy and stability, making them suitable for various environmental research applications. The low-cost nature of Arduino components and the open-source ecosystem facilitate customization and scalability, enabling researchers and organizations to develop tailored monitoring solutions for specific environmental contexts. Additionally, the paper discusses the challenges and limitations encountered during the deployment of Arduino-based monitoring systems, including power constraints, sensor calibration, and data processing techniques. Furthermore, it explores potential avenues for improvement and optimization to enhance the system's capabilities and data accuracy. In conclusion, this research

关键词： Internet of things

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Comparative Analysis of Robotic Arm Efficiency: Evaluating PID and Fuzzy-PID Control using Printed Mechanical Structure and ESP8266 Integration 6

Comparative Analysis of Robotic Arm Efficiency: Evaluating P...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Crisnapati, Padma Nyoman Susila, I. Made Darma Aryanto, I. Komang Agus Ady Krohkaew, Jaturapith Diaz, Ricky Aurelius Nurtanto Pande, I. Made Suandana Astika Department of Information Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Department of Computer Systems Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Department of Information Technology Faculty of Informatics and Computers Institute Technology and Business STIKOM Bali Denpasar Indonesia Dept. of Big Data Management and Analytics Faculty of Science and Technology Rajamangala University of Technology Thanyaburi Thanyaburi Thailand

ISBN: (纸本)9798331539603

This study investigates the comparative effectiveness of manual PID tuning versus Fuzzy-PID control in enhancing the performance of a robot arm with two degrees of freedom (2-DOF). The experimental approach involves conducting two scenarios: manual tuning of PID parameters in Scenario 1 and utilizing a fuzzy algorithm for PID parameter tuning in Scenario 2. The novelty of this research lies in the new 3D mechanical design of the 2-DOF robot arm and the utilization of the ESP8266 microcontroller for implementing Fuzzy-PID control. Performance evaluation is conducted using Root Mean Square Error (RMSE) and Root Mean Square Percentage Error (RMSPE) calculations. The results reveal that Fuzzy-PID control significantly reduces errors compared to manual PID tuning, with lower RMSE and RMSPE values indicating more precise and stable control. These findings underscore the potential of integrating fuzzy logic with PID control to enhance system adaptability and flexibility. Additionally, the ESP8266 is capable of handling the computations required for fuzzy-PID control while being cost-effective. © 2024 IEEE.

关键词： Robotic arms

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Artificial intelligence in physiological characteristics recognition for internet of things authentication

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Digital Communications and Networks 2024年第3期10卷 740-755页

作者： Zhimin Zhang Huansheng Ning Fadi Farha Jianguo Ding Kim-Kwang Raymond Choo School of Computer and Communication Engineering University of Science and Technology BeijingBeijing100083China Beijing Engineering Research Center for Cyberspace Data Analysis and Applications Beijing100083China Faculty of Informatics Engineering Aleppo UniversityAleppoSyria Department of Computer Science Blekinge Institute of Technology37179KarlskronaSweden Department of Information Systems and Cyber Security University of Texas at San AntonioSan AntonioTX78249-0631USA

Effective user authentication is key to ensuring equipment security,data privacy,and personalized services in Internet of Things(IoT)***,conventional mode-based authentication methods(e.g.,passwords and smart cards)may be vulnerable to a broad range of attacks(e.g.,eavesdropping and side-channel attacks).Hence,there have been attempts to design biometric-based authentication solutions,which rely on physiological and behavioral *** characteristics need continuous monitoring and specific environmental settings,which can be challenging to implement in ***,we can also leverage Artificial Intelligence(AI)in the extraction and classification of physiological characteristics from IoT devices processing to facilitate ***,we review the literature on the use of AI in physiological characteristics recognition pub-lished after *** use the three-layer architecture of the IoT(i.e.,sensing layer,feature layer,and algorithm layer)to guide the discussion of existing approaches and their *** also identify a number of future research opportunities,which will hopefully guide the design of next generation solutions.

关键词： Physiological characteristics recognition Artificial intelligence Internet of things Biological-driven authentication

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Reducing Uncertainty in 3D Medical Image Segmentation under Limited Annotations through Contrastive Learning 7

Reducing Uncertainty in 3D Medical Image Segmentation under ...

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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Karimijafarbigloo, Sanaz Azad, Reza Kazerouni, Amirhossein Merhof, Dorit Faculty of Informatics and Data Science University of Regensburg Regensburg Germany RWTH Aachen University Aachen Germany School of Electrical Engineering Iran University of Science and Technology Tehran Iran Fraunhofer Institute for Digital Medicine MEVIS Bremen Germany

Despite recent successes in semi-supervised learning for natural image segmentation, applying these methods to medical images presents challenges in obtaining discriminative representations from limited annotations. While contrastive learning frameworks excel in similarity measures for classification, their transferability to precise pixel-level segmentation in medical images is hindered, particularly when confronted with inherent prediction uncertainty. To overcome this issue, our approach incorporates two subnetworks to rectify erroneous predictions. The first network identifies uncertain predictions, generating an uncertainty attention map. The second network employs an uncertainty-aware descriptor to refine the representation of uncertain regions, enhancing the accuracy of predictions. Additionally, to adaptively recalibrate the representation of uncertain candidates, we define class prototypes based on reliable predictions. We then aim to minimize the discrepancy between class prototypes and uncertain predictions through a deep contrastive learning strategy. Our experimental results on organ segmentation from clinical MRI and CT scans demonstrate the effectiveness of our approach compared to state-of-the-art methods. Code. © 2024 CC-BY 4.0, S.K., R.A., A.K. & D.M.

关键词： Contrastive Learning

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Optimized Artificial Neural Network-Based Control Strategy for Boost Converters

Optimized Artificial Neural Network-Based Control Strategy f...

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2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Panahidoost, Reza Mirshekali, Hamid Dashti, Rahman Samsami, Reza Rezaei, Mohammad Hossein Shaker, Hamid Reza Clinical Laboratory Center of Power Systems & Protection Faculty of Intelligent Systems Engineering & Data Science Persian Gulf University Bushehr*** Iran Center of Energy Informatics University of Southern Demark Odense Denmark

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Due to the rapid development of adaptive control and industrial automation science, the necessity of the boost converters is perceiving more and more, accordingly, designing a controller for these components which is dynamically characterized could be advantageous both theoretically and practically. Consequently, A machine learning-based controller using an artificial neural network (ANN) is designed, which is able to regulate the voltage of DC-DC Boost converters. In this paper, the primary controller is a linear quadratic regulator (LQR) based controller which is replaced by an ANN controller after generating training and testing data. After training the neural network, the Genetic Algorithm (GA) and an integral control action are used to minimize the system's overshoot and steady-state error, respectively. All in all, for the performance validation and making comparison accurately, the output voltage of a boost converter controlled by the optimized ANN model is simulated in the MATLAB/Simulink, which is conclusive that the new ANN controller can track the reference voltage properly. © 2022 IEEE.

关键词： Genetic algorithms

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Soft Computing Applications 1

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丛书名： Advances in Intelligent Systems and Computing

1000年

作者： Valentina Emilia Balas Lakhmi C. Jain Branko Kovačević

<;Management

ISBN: (数字)9783319182964

ISBN: (纸本)9783319182957

<;Management

关键词： Computational Intelligence Artificial Intelligence

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Machine Learning-Based Control Framework for Boost Converters Applying Particle Swarm Optimization

Machine Learning-Based Control Framework for Boost Converter...

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2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2022

作者： Rezaei, Mohammad Hossein Mirshekali, Hamid Dashti, Rahman Samsami, Reza Panahidoost, Reza Shaker, Hamid Reza Clinical Laboratory Center of Power Systems & Protection Faculty of Intelligent Systems Engineering & Data Science Persian Gulf University Bushehr*** Iran Center of Energy Informatics University of Southern Demark Odense Denmark

ISBN: (数字)9781665470957

ISBN: (纸本)9781665470957

Due to the shortage of fossil fuels and their jeopardizing environmental effects, microgrids have gained popularity in the recent century since they make use of renewable energy sources. DC-DC boost converters have been applied in microgrids to satisfy Loads' needs. Although classic controllers have been used to control output voltage, they are usually not effective when the plant model changes. One such controller is model predictive control (MPC) which controls output based on predictions. As such, machine learning methods have been proposed to counter classic controllers' limitations. In this paper, a decision tree model is developed to control output voltage with the help of a Proportional Integral (PI) controller which is tuned by the Particle Swarm Optimization (PSO) algorithm to remove the steady-state error. Required data for training the model is prepared by a DC-DC boost converter controlled by an MPC, then the tree model is trained and tested in MATLAB, after which it is implemented in the Simulink model previously controlled by the MPC. Finally, simulation results are provided as voltage reference and load change to show the system's reliability. © 2022 IEEE.

关键词： Decision trees

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