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Brain-Controlled Computer Tasks for Paralyzed Persons: Framework Overview

Procedia Computer Science 2024年 237卷 28-35页

作者： Amna Imran Haider Chaudhary Maimoona Tariq Zahra Ali Abdul Ahad Hammad Naqvi Paulo Jorge Coelho Ivan Miguel Pires Knowledge Unit of Science and Technology University of Management and Technology Sialkot Pakistan School of Software Northwestern Polytechnical University Xian Shaanxi 710072 P. R. China Department of Computer Science Istanbul Technical University Turkey Polytechnic of Leiria Leiria Portugal Institute for Systems Engineering and Computers at Coimbra Coimbra Portugal Instituto de Telecomunicações Universidade da Beira Interior Covilhã Portugal

To bridge the digital barrier between those with disabilities and those without impairments, several applications can be operated by brain signals. It highlights the importance of employing brain-computer interfaces (BCI) and mentions a specific system called the "Brain-Controlled Computer System" that can control a computer cursor and perform mouse actions using brain signals.. The system can control the cursor and provide mouse left/right click with brain signals using a low-cost Brainlink Lite device. based on this system, we employed machine learning and deep learning methodologies to develop a system that classifies brain signals for cursor movement. The methodology involved gathering user input through surveys, interviews, and comparison studies, as well as utilizing pre-developed datasets collected using a mind sensor. We initially trained a model on a previous dataset, achieving approximately 70% accuracy. However, when applied to the new dataset, the accuracy dropped to 49%. To improve the results, we experimented with more complex models, including additional layers and a dropout layer to prevent overfitting. Despite these enhancements, the accuracy only reached a maximum of 52%. The research team collected a dataset tailored to their specific application. They made a groundbreaking discovery by applying a convolutional neural network (CNN), typically used for image analysis, to process continuous brainwave data. Despite this non-standard use, their hypothesis proved correct, with the CNN model achieving an impressive 80% accuracy in classifying brain signals for controlling a cursor.

关键词： Brain-Controlled Brainlink Lite EEG Machine Learning Deep learning

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A modified Weibull model for service life prediction and spare parts forecast in heat treatment industry 10th

A modified Weibull model for service life prediction and spa...

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10th CIRP Sponsored Conference on Digital Enterprise Technologies - Digital Technologies as Enablers of Industrial Competitiveness and Sustainability, DET 2020

作者： Kovacs, Klaudia Ansari, Fazel Sihn, Wilfried Fraunhofer Austria Research GmbH Division of Production and Logistics Management Vienna1040 Austria Tu Wien Institute of Management Science Research Group of Smart and Knowledge-Based Maintenance Vienna1040 Austria

The ongoing digitization of the industry leads to new opportunities in industrial maintenance. Several concepts for smart maintenance have been published in literature, however, their application remains limited in industrial practice. To close the existing gap between research and practice, a novel practice-oriented approach is presented. A modified Weibull model is developed and embodied into a digital assistance system to visualize and predict remaining service life and to forecast the required number of spare parts. The developed model has been implemented in the use case in the heat treatment industry, and the results are presented to demonstrate the effectiveness of the proposed approach. © 2021 The Author(s).

关键词： Weibull distribution

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A Spatial-Temporal Attention Model for Human Trajectory Prediction

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 965-974页

作者： Xiaodong Zhao Yaran Chen Jin Guo Dongbin Zhao the School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083 the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing 100083China

Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.

关键词： Attention mechanism long-short term memory(LSTM) spatial-temporal model trajectory prediction

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Examining the impact of COVID-19 on health care utilization among persons with chronic spinal cord injury/dysfunction: a population study ( vol 61 , pg 562 ,2023)

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SPINAL CORD 2024年第4期62卷 197-198页

作者： Senthinathan, Arrani Tadrous, Mina Hussain, Swaleh Craven, B. Catharine Jaglal, Susan B. Moineddin, Rahim Shepherd, John Cadel, Lauren Noonan, Vanessa K. McKay, Sandra Tu, Karen Guilcher, Sara J. T. Department of Family and Community Medicine University of Toronto Toronto ON Canada ICES Toronto ON Canada North York General Hospital Toronto ON Canada Toronto Western Family Health Team University Health Network Toronto ON Canada Women’s College Hospital Institute for Health Systems Solutions and Virtual Care Women’s College Hospital Toronto ON Canada Leslie Dan Faculty of Pharmacy University of Toronto Toront ON Canada Institute of Health Policy Management and Evaluation University of Toronto Toronto ON Canada Rehabilitation Science Institute and Department of Physical Therapy Temerty Faculty of Medicine University of Toronto Toronto ON Canada KITE (Knowledge Innovation Talent Everywhere) Toronto Rehabilitation Institute - University Health Network Toronto ON Canada Department of Medicine Faculty of Medicine University of Toronto Toronto ON Canada Spinal Cord Rehabilitation Program Toronto Rehabilitation Institute – University Health Network Toronto ON Canada Praxis Spinal Cord Institute Vancouver BC Canada International Collaboration on Repair Discoveries Vancouver BC Canada VHA Home HealthCare Toronto ON Canada

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Composing complex and hybrid AI solutions

arXiv

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arXiv 2022年

作者： Schüller, Peter Costeira, João Paulo Crowley, James Grosinger, Jasmin Ingrand, Félix Köckemann, Uwe Saffotti, Alessandro Welss, Martin Knowledge-based Systems Group Technische Universität Wien Vienna Austria Institute for Systems and Robotics Técnico Lisboa Lisbon Portugal Univ. Grenoble Alpes CNRS Inria Grenoble France Center for Applied Autonomous Sensor Systems Örebro University Sweden LAAS-CNRS University of Toulouse France Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Germany

Progress in several areas of computer science has been enabled by comfortable and efficient means of experimentation, clear interfaces, and interchangable components, for example using OpenCV for computer vision or ROS for robotics. We describe an extension of the Acumos system towards enabling the above features for general AI applications. Originally, Acumos was created for telecommunication purposes, mainly for creating linear pipelines of machine learning components. Our extensions include support for more generic components with gRPC/Protobuf interfaces, automatic orchestration of graphically assembled solutions including control loops, sub-component topologies, and event-based communication, and provisions for assembling solutions which contain user interfaces and shared storage areas. We provide examples of deployable solutions and their interfaces. The framework is deployed at http://***/ and its source code is managed as an open source Eclipse project. © 2022, CC BY.

关键词： User interfaces

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Using finite-state machines to automatically scan classical Greek hexameter

arXiv

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arXiv 2021年

作者： Schumann, Anne-Kathrin Beierle, Christoph Blößner, Norbert T2K: Text to Knowledge Dresden Germany Faculty of Mathematics and Computer Science Knowledge-Based Systems FernUniversität in Hagen Hagen Germany Institute of Greek and Latin Languages and Literatures Freie Universität Berlin Berlin Germany

This paper presents a fully automatic approach to the scansion of Classical Greek hexameter verse. In particular, the paper describes an algorithm that uses deterministic finite-state automata and local linguistic rules to implement a targeted search for valid spondeus patterns and, in addition, a weighted finite-state transducer to correct and complete partial analyses and to reject invalid candidates. The paper also details the results of an empirical evaluation of the annotation quality resulting from this approach on hand-annotated data. It is shown that a finite-state approach provides quick and linguistically sound analyses of hexameter verses as well as an efficient formalisation of linguistic knowledge. The project code is available online1 Copyright © 2021, The Authors. All rights reserved.

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The effect of online meeting and health screening on business travel: A stated preference case study in Hong Kong

The effect of online meeting and health screening on busines...

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作者： Chen, Tiantian Fu, Xiaowen Hensher, David A. Li, Zhi-Chun Sze, N.N. Department of Industrial and Systems Engineering The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Behaviour and Knowledge Engineering Research Centre Department of Industrial and Systems Engineering Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong Institute of Transport and Logistics Studies the University of Sydney Business School the University of Sydney Australia School of Management Huazhong University of Science and Technology Wuhan China Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong

This study quantifies the effects of health control measures at the airport on passenger behaviour related to business travel. A stated preference survey was conducted over potential air travellers in Hong Kong in the context of COVID-19 pandemic. Panel latent class models were estimated to understand passenger preference toward new travel requirements given the applicability of online meeting. Online meeting is applicable in cases where it is a good substitute of air travel and achieves the same outcomes of a trip, and inapplicable otherwise. Empirical results indicate that traveller subgroups are affected in different ways. When an online meeting is inapplicable, nearly 75% of the respondents prefer to travel for business and undertake health screenings. These passengers (identified as "captive" business travellers) perceive such measures necessary to lower health related risks during air travel. As such, they are willing to spend up to 21 to 38 min on the health control measures such as vaccination record requirements and test involving sample collection. When an online meeting is applicable, the share of "choice" business travellers is about 45%, among whom the attitudes towards health control measures become more averse. The average weighted willingness-to-pay for the time saved at health checkpoints increase significantly. The aviation industry thus faces a "double-hit" problem: operation costs will increase due to pandemic control measures, and the resultant inconvenience, extra time and costs further reduces travel demand. Unlike previous short pandemics, business travel is likely to suffer with an extended decline until the pandemic is fully controlled. These identified challenges call for financial and operational support to help the aviation industry reach a sustainable "new normal". The high value of time saved at check points also justifies investments that make the pandemic control and health measures efficient and smooth. Travellers’ time spent on air

关键词： COVID-19

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Parameterizing neural networks for disease classification

Parameterizing neural networks for disease classification

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作者： Bahra, Guryash Wiese, Lena Institute of Computer Science University of Göttingen Göttingen Germany L3S Research Center/Knowledge Based Systems Group Leibniz University Hannover Hannover Germany

Neural networks are one option to implement decision support systems for health care applications. In this paper, we identify optimal settings of neural networks for medical diagnoses: The study involves the application of supervised machine learning using an artificial neural network to distinguish between gout and leukaemia patients. With the objective to improve the base accuracy (calculated from the initial set-up of the neural network model), several enhancements are analysed, such as the use of hyperbolic tangent activation function instead of the sigmoid function, the use of two hidden layers instead of one, and transforming the measurements with linear regression to obtain a smoothened data set. Another setting we study is the impact on the accuracy when using a data set of reduced size but with higher data quality. We also discuss the tradeoff between accuracy and runtime efficiency. © 2019 The Authors. Expert systems published by John Wiley & Sons Ltd

关键词： Expert systems

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Yield prediction in semiconductor manufacturing using an AI-based cascading classification system

Yield prediction in semiconductor manufacturing using an AI-...

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IEEE International Conference on Electro-Information Technology

作者： Peter Stich Michael Wahl Peter Czerner Christian Weber Madjid Fathi Digital Integrated Systems University of Siegen Siegen Germany Elmos AG Dortmund Germany Knowledge Based Systems and Knowledge Management University of Siegen Siegen Germany

ISBN: (数字)9781728153179

ISBN: (纸本)9781728153186

The production of a wafer is a highly complex process with hundreds of individual, sequential process steps and thousands of parameters, where each of them can have an influence on the final product. One approach for yield prediction before or during production is based on the divide-and-control principle. The individual process steps are considered independently and based on the analysis of these steps a prediction for the overall yield is calculated by a master system. In this article, a novel concept for predicting the yield is presented, which combines a cascading prediction algorithm, based on the sequential process steps, with an artificial intelligence (AI) focused master system. The system also proposes a recommended selection for optimal yield if the next process step can be executed on different machines/ chambers.

关键词： Manufacturing Production Artificial intelligence Classification algorithms Forecasting Process control Semiconductor device measurement

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Rmagine: 3D Range Sensor Simulation in Polygonal Maps via Raytracing for Embedded Hardware on Mobile Robots

arXiv

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arXiv 2022年

作者： Mock, Alexander Wiemann, Thomas Hertzberg, Joachim Knowledge Based Systems Group Institute of Computer Science Osnabrück University Berghoffstraße 11 Osnabrück49090 Germany Autonomous Robotics Group Institute of Computer Science Osnabrück University Berghoffstraße 11 Osnabrück49090 Germany DFKI Niedersachsen Plan-based Robot Control Berghoffstraße 11 Osnabrück49090 Germany

Sensor simulation has emerged as a promising and powerful technique to find solutions to many real-world robotic tasks like localization and pose tracking. However, commonly used simulators have high hardware requirements and are therefore used mostly on high-end computers. In this paper, we present an approach to simulate range sensors directly on embedded hardware of mobile robots that use triangle meshes as environment map. This library called Rmagine allows a robot to simulate sensor data for arbitrary range sensors directly on board via raytracing. Since robots typically only have limited computational resources, the Rmagine aims at being flexible and lightweight, while scaling well even to large environment maps. It runs on several platforms like Laptops or embedded computing boards like Nvidia Jetson by putting an unified API over the specific proprietary libraries provided by the hardware manufacturers. This work is designed to support the future development of robotic applications depending on simulation of range data that could previously not be computed in reasonable time on mobile systems. Copyright © 2022, The Authors. All rights reserved.

关键词： Mobile robots

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