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arXiv 2022年

作者： Bilik, Simon Zemcik, Tomas Kratochvila, Lukas Ricanek, Dominik Richter, Miloslav Zambanini, Sebastian Horak, Karel Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Technická 3058/10 Brno61600 Czech Republic Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Yliopistonkatu 34 Lappeenranta53850 Finland Computer Vision Lab Institute of Visual Computing & Human-Centered Technology Faculty of Informatics TU Wien Favoritenstr. 9/193-1 ViennaA-1040 Austria

Wide use and availability of machine learning and computer vision techniques allows development of relatively complex monitoring systems in many domains. Besides the traditional industrial domain, new applications appears also in biology and agriculture, where they may be used to detect infections, parasites and weeds, but also for automated monitoring and early warning systems. This goes in concordance with the introduction of the easily accessible hardware and development kits such as the Arduino, or RaspberryPi families. In this paper, we survey 50 papers focusing on the methods of automated beehive monitoring using computer vision techniques. Particularly on the pollen and Varroa mite detection together with the bee traffic monitoring. Such systems could also be used for monitoring of honeybee colonies and for the inspection of their health state, which could potentially identify dangerous states before the situation is critical, or to better plan periodic bee colony inspections and therefore save significant costs. Further on, we also include analysis of the research trends in this application field and we outline the possible directions of new development. Our paper is also aimed at veterinary and apidology professionals and experts, who may not be familiar with machine learning to introduce them to its capabilities, hence each family of techniques is prefaced by a brief theoretical introduction and motivation related to its base method. We hope that this paper will inspire other scientists to use machine learning techniques for other applications in beehive monitoring. © 2022, CC BY.

关键词： Computer vision

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Deep latent low-rank fusion network for progressive subspace discovery 29

Deep latent low-rank fusion network for progressive subspace...

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29th International Joint Conference on Artificial Intelligence, IJCAI 2020

作者： Zhang, Zhao Ren, Jiahuan Zhang, Zheng Liu, Guangcan Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology China School of Computer Science and Technology Soochow University China Bio-Computing Research Center Harbin Institute of Technology Shenzhen China School of Information and Control Nanjing University of Information Science and Technology China

ISBN: (纸本)9780999241165

Low-rank representation is powerful for recovering and clustering the subspace structures, but it cannot obtain deep hierarchical information due to the single-layer mode. In this paper, we present a new and effective strategy to extend the single-layer latent low-rank models into multiple-layers, and propose a new and progressive Deep Latent Low-Rank Fusion Network (DLRF-Net) to uncover deep features and structures embedded in input data. The basic idea of DLRF-Net is to refine features progressively from the previous layers by fusing the subspaces in each layer, which can potentially obtain accurate features and subspaces for representation. To learn deep information, DLRF-Net inputs shallow features of the last layers into subsequent layers. Then, it recovers the deeper features and hierarchical information by congregating the projective subspaces and clustering subspaces respectively in each layer. Thus, one can learn hierarchical subspaces, remove noise and discover the underlying clean subspaces. Note that most existing latent low-rank coding models can be extended to multilayers using DLRF-Net. Extensive results show that our network can deliver enhanced performance over other related frameworks. © 2020 Inst. Sci. inf., Univ. Defence in Belgrade. All rights reserved.

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A New Method for Quantifying Workload of Drivers and Application in Analyzing Stabilization Time after Critical Events in Varying Automation Levels

SSRN

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SSRN 2022年

作者： Zhai, Junda Lu, Guangquan Chen, Facheng Liu, Miaomiao School of Transportation Science and Engineering Beihang University Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Department of Traffic Management School People’s Public Security University of China Beijing100038 China Beijing100088 China

The workload of drivers increases rapidly after the critical events and the period of time for workload to stabilize is worthy to investigate due to the close relationship between the workload and driving performance of drivers. It is yet unclear if the stabilization time of drivers varies across the different automation levels. In previous studies, a number of measurements have been utilized in evaluating the workload of drivers while most of the existing methods have limitations in studying the stabilization time. Therefore, this study introduces a new method for quantifying the cognitive workload of drivers with a measurement frequency of 2 seconds based on the performance of drivers to a newly designed calculating NDRT. A driving simulator experiment containing three automated driving levels and two critical events was conducted for the verification and application of the quantification method. Data were collected from 52 participants. The workload before and after critical events was analyzed and the results of quantified workload, the measurement acquired through the quantification method, showed a high similarity with heart rate and pupil diameter, which supported that the measurement could well reflect the workload of drivers with and without critical events. Moreover, the quantification method was applied to analyzing stabilization time after the critical situations and it was observed that quantified workload was a more appropriate measurement in determining the stabilization time of workload compared to heart rate and pupil diameter. In addition, the analysis results based on the workload quantification method showed that the effect of automation levels on the stabilization time of drivers after the critical events occurred was not significant. © 2022, The Authors. All rights reserved.

关键词： Automobile drivers

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Initial Study on Implementation of Smoothing Filters for the Purpose of Bioimpedance Spectroscopy Parameters Classification

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IFAC-PapersOnLine 2022年第4期55卷 393-398页

作者： Aleksandra Kawala-Sterniuk Amir F. Al-Bakri Mariusz Pelc Katarzyna Cichoń Wojciech Chlewicki Stepan Ozana Radek Martinek Jakub Możaryn Volodymyr Khoma Halyna Kenyo Edward Jacek Gorzelańczyk Michał Podpora Jarosław Zygarlicki Opole University of Technology Faculty of Electrical Engineering Automatic Control and Informatics 45-758 Opole Poland Department of Biomedical Engineering College of Engineering University of Babylon 51001 Babylon Iraq University of Greenwich School of Computing and Mathematical Sciences SE10 9LS London UK West Pomeranian University of Technology in Szczecin Faculty of Electrical Engineering 70-313 Szczecin Poland Faculty of Mechatronics Warsaw University of Technology 02-525 Warsaw Poland Lviv Polytechnic National University Institute of Computer Technologies Automation and Metrology Lviv Ukraine Institute of Philosophy Kazimierz Wielki University 85-092 Bydgoszcz Poland Faculty of Mathematics and Computer Science Adam Mickiewicz University in Poznań 61-614 Poznań Poland

Bioimpedance is a commonly used method for various conditions monitoring. In this paper, the authors carried out some research where they implemented various smoothing filters to enable identification of the bioimpedance spectroscopy parameters. The proposed filtering methods may also be used for application on embedded systems, which have smaller computing power but have become recently very popular. The obtained results with the implementation of smoothing filters were promising, however, some of the obtained results were unsatisfactory. This work also contains a brief introduction to bioimpedance spectroscopy and smoothing filters.

关键词： Bioimpedance Spectroscopy smoothing-filtering Savitzky-Golay Filter Median Filtering Moving-Average Filters

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Exploring the etiology of auditory-verbal hallucinations through air- and bone-conducted self-other voice discrimination, a pilot study with preliminary results

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Neuroscience Applied 2024年 3卷

作者： I. Muselimović M. Bagarić J. Vukojević A. Savić P. Brečić L. Jelić P. Orepić University Psychiatric Hospital Vrapče Department for Diagnostics and Intensive care Zagreb Croatia University Psychiatric Hospital Vrapče Department for Affective disorders Zagreb Croatia Faculty of Electrical Engineering and Computing Department of Control and Computer Engineering Zagreb Croatia University of Geneva Campus Biotech Geneva Switzerland

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A multi-resolution frontier-based planner for autonomous 3D exploration

arXiv

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arXiv 2020年

作者： Batinović, Ana Petrović, Tamara Ivanovic, Antun Petric, Frano Bogdan, Stjepan University of Zagreb Faculty of Electrical Engineering and Computing LARICS Laboratory for Robotics and Intelligent Control Systems Unska 3 Zagreb10000 Croatia

In this paper we propose a planner for 3D exploration that is suitable for applications using state-of-the-art 3D sensors such as lidars, which produce large point clouds with each scan. The planner is based on the detection of a frontier - a boundary between the explored and unknown part of the environment - and consists of the algorithm for detecting frontier points, followed by clustering of frontier points and selecting the best frontier point to be explored. Compared to existing frontier-based approaches, the planner is more scalable, i.e. it requires less time for the same data set size while ensuring similar exploration time. Performance is achieved by not relying on data obtained directly from the 3D sensor, but on data obtained by a mapping algorithm. In order to cluster the frontier points, we use the properties of the Octree environment representation, which allows easy analysis with different resolutions. The planner is tested in the simulation environment and in an outdoor test area with a UAV equipped with a lidar sensor. The results show the advantages of the approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Conformal mapping

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Unlocking the Secrets Behind Advanced Artificial Intelligence Language Models in Deidentifying Chinese-English Mixed Clinical Text: Development and Validation Study

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Journal of Medical Internet Research 2024年第1期26卷 e48443页

作者： Lee, You-Qian Chen, Ching-Tai Chen, Chien-Chang Lee, Chung-Hong Chen, Peitsz Wu, Chi-Shin Dai, Hong-Jie Dialogue System Technical Department Asustek Computer Inc Taipei Taiwan Intelligent System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan Center for Precision Health Research Asia University Taichung Taiwan Electromagnetic Sensing Control and AI Computing System Laboratory Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Knowledge Discovery and Data Mining Lab Department of Electrical Engineering College of Electrical Engineering and Computer Science National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Chemical Engineering Feng Chia University Taichung Taiwan National Center for Geriatrics and Welfare Research National Health Research Institutes Zhunan Taiwan National Institute of Cancer Research National Health Research Institutes Tainan Taiwan School of Post-Baccalaureate Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Center for Big Data Research Kaohsiung Medical University Kaohsiung Taiwan

Background: The widespread use of electronic health records in the clinical and biomedical fields makes the removal of protected health information (PHI) essential to maintain privacy. However, a significant portion of information is recorded in unstructured textual forms, posing a challenge for deidentification. In multilingual countries, medical records could be written in a mixture of more than one language, referred to as code mixing. Most current clinical natural language processing techniques are designed for monolingual text, and there is a need to address the deidentification of code-mixed text. Objective: The aim of this study was to investigate the effectiveness and underlying mechanism of fine-tuned pretrained language models (PLMs) in identifying PHI in the code-mixed context. Additionally, we aimed to evaluate the potential of prompting large language models (LLMs) for recognizing PHI in a zero-shot manner. Methods: We compiled the first clinical code-mixed deidentification data set consisting of text written in Chinese and English. We explored the effectiveness of fine-tuned PLMs for recognizing PHI in code-mixed content, with a focus on whether PLMs exploit naming regularity and mention coverage to achieve superior performance, by probing the developed models’ outputs to examine their decision-making process. Furthermore, we investigated the potential of prompt-based in-context learning of LLMs for recognizing PHI in code-mixed text. Results: The developed methods were evaluated on a code-mixed deidentification corpus of 1700 discharge summaries. We observed that different PHI types had preferences in their occurrences within the different types of language-mixed sentences, and PLMs could effectively recognize PHI by exploiting the learned name regularity. However, the models may exhibit suboptimal results when regularity is weak or mentions contain unknown words that the representations cannot generate well. We also found that the availability of cod

关键词： ChatGPT code mixing deidentification electronic health record large language model pretrained language model

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An Image Denoising Method Based on Deep Residual GAN 4

An Image Denoising Method Based on Deep Residual GAN

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2020 4th International Workshop on Advanced Algorithms and control engineering, IWAACE 2020

作者： Wang, Ziyuan Wang, Lidan Duan, Shukai Li, Yunfei Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing Chongqing400715 China Schoolof Electronic and Information Engineering Southwest University Chongqing400715 China College of Artificiall Intelligence Southwest University Chongqing400715 China Braininspired Computing and Intelligent Control of Chongqing Key Lab Chongqing400715 China National and Local Joint Engineering Laboratory of Intelligent Transmission and Control Technology Chongqing400715 China Chongqing Brain Science Collaborative Innovation Center Chongqing400715 China

As people come into contact with image data more often, high quality and clear images attract more attention. Many methods have been proposed to deal with image noise problem including deep learning (DL). However most of them is lack of capability when customers want more perceptual details of the image without information loss. In this paper, a deep residual network based on generative adversarial (GAN) network was proposed to complete the image denoising mission. Firstly, a generative-adversarial network structure based on residual blocks was designed. Secondly, a refined loss function was given to train the GAN network. The well designed loss function can help the generated image to be very close to the clear counterpart (ground truth) while enhancing more details in colours and brightness. Finally, extensive experiments show that our network is not only convincing for images denoising, but also effective for other image process tasks, such as image defogging, medical CT denoising etc., presenting impressive and competitive effects. © Published under licence by IOP Publishing Ltd.

关键词： Image denoising

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FaSTrack: A modular framework for real-time motion planning and guaranteed safe tracking

arXiv

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arXiv 2021年

作者： Chen, Mo Herbert, Sylvia L. Hu, Haimin Pu, Ye Fisac, Jaime F. Bansal, Somil Han, SooJean Tomlin, Claire J. School of Computing Science Simon Fraser University Canada Department of Mechanical and Aerospace Engineering UC San Diego United States Department of Electrical Engineering Princeton University United States Department of Electrical and Electronic Engineering University of Melbourne Australia Department of Electrical Engineering and Computer Sciences UC Berkeley United States Control and Dynamical Systems Program California Institute of Technology United States

Real-time, guaranteed safe trajectory planning is vital for navigation in unknown environments. However, real-time navigation algorithms typically sacrifice robustness for computation speed. Alternatively, provably safe trajectory planning tends to be too computationally intensive for real-time replanning. We propose FaSTrack, Fast and Safe Tracking, a framework that achieves both real-time replanning and guaranteed safety. In this framework, real-time computation is achieved by allowing any trajectory planner to use a simplified planning model of the system. The plan is tracked by the system, represented by a more realistic, higher-dimensional tracking model. We precompute the tracking error bound (TEB) due to mismatch between the two models and due to external disturbances. We also obtain the corresponding tracking controller used to stay within the TEB. The precomputation does not require prior knowledge of the environment. We demonstrate FaSTrack using Hamilton-Jacobi reachability for precomputation and three different real-time trajectory planners with three different tracking-planning model pairs. Copyright © 2021, The Authors. All rights reserved.

关键词： Trajectories

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Heterogeneous influence of individuals’ behavior on mask efficacy in gathering environments

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Frontiers of engineering Management 2022年第4期9卷 550-562页

作者： Haochen SUN Xiaofan LIU Zhanwei DU Ye WU Haifeng ZHANG Xiaoke XU College of Information and Communication Engineering Dalian Minzu UniversityDalian 116600China Web Mining Laboratory Department of Media and CommunicationCity University of Hong KongHong KongChina WHO Collaborating Centre for Infectious Disease Epidemiology and Control School of Public HealthLi Ka Shing Faculty of MedicineThe University of Hong KongHong KongChina Laboratory of Data Discovery for Health Hong Kong Science and Technology ParkHong KongChina Computational Communication Research Center Beijing Normal University(Zhuhai)Zhuhai 519087China School of Journalism and Communication Beijing Normal UniversityBeijing 100875China Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical ScienceAnhui UniversityHefei 230601China

Wearing masks is an easy way to operate and popular measure for preventing *** masks can slow down the spread of viruses,their efficacy in gathering environments involving heterogeneous person-to-person contacts remains ***,we aim to investigate the epidemic prevention effect of masks in different real-life gathering *** study uses four real interpersonal contact datasets to construct four empirical networks to represent four gathering *** transmission of COVID-19 is simulated using the Monte Carlo simulation *** heterogeneity of individuals can cause mask efficacy in a specific gathering environment to be different from the baseline efficacy in general ***,the heterogeneity of gathering environments causes the epidemic prevention effect of masks to *** masks can greatly reduce the probability of clustered epidemics and the infection scale in primary schools,high schools,and ***,the use of masks alone in primary schools and hospitals cannot control *** high schools with social distancing between classes and in workplaces where the interpersonal contact is relatively sparse,masks can meet the need for *** the heterogeneity of individual behavior,if individuals who are more active in terms of interpersonal contact are prioritized for mask-wearing,the epidemic prevention effect of masks can be ***,asymptomatic infection has varying effects on the prevention effect of masks in different *** effect can be weakened or eliminated by increasing the usage rate of masks in high schools and ***,the effect on primary schools and hospitals cannot be *** study contributes to the accurate evaluation of mask efficacy in various gathering environments to provide scientific guidance for epidemic prevention.

关键词： COVID-19 masks behavioral heterogeneity asymptomatic infection

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