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Hard hat wearing detection based on head keypoint localization

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arXiv 2021年

作者： Wójcik, Bartosz Zarski, Mateusz Ksiązek, Kamil Miszczak, Jaroslaw A. Skibniewski, Miroslaw J. Faculty of Civil Engineering Silesian University of Technology Poland Institute of Theoretical and Applied Informatics Polish Academy of Sciences Poland Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Poland Department of Civil & Environmental Engineering University of Maryland College Park United States

In recent years, a lot of attention is paid to deep learning methods in the context of vision-based construction site safety systems. However, still there is no reliable way to establish the relationship between supervised construction workers and their basic personal protective equipment, like hard hats. To address this problem, a novel deep learning method combining object detection, keypoint localization, and simple rule-based reasoning is proposed in this article. In tests, this solution surpassed the previous methods based on the relative bounding box position of different instances, as well as direct detection of hard hat wearers and non-wearers. Achieving MS COCO style overall AP of 67.5% compared to 66.4% and 66.3% achieved by the above-mentioned approaches, with class specific AP for hard hat non-wearers of 64.1% compared to 63.0% and 60.3%. The results show that the conjunction of deep learning methods with humanly-interpretable rule-based algorithm is better suited for detection of hard hat non-wearers. Code is available at barwojcik/hard-hats. © 2021, CC BY.

关键词： Object detection

Efficient MedSAMs: Segment Anything in Medical Images on Laptop

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arXiv 2024年

作者： Ma, Jun Li, Feifei Kim, Sumin Asakereh, Reza Le, Bao-Hiep Nguyen-Vu, Dang-Khoa Pfefferle, Alexander Wei, Muxin Gao, Ruochen Lyu, Donghang Yang, Songxiao Purucker, Lennart Marinov, Zdravko Staring, Marius Lu, Haisheng Dao, Thuy Thanh Ye, Xincheng Li, Zhi Brugnara, Gianluca Vollmuth, Philipp Foltyn-Dumitru, Martha Cho, Jaeyoung Mahmutoglu, Mustafa Ahmed Bendszus, Martin Pflüger, Irada Rastogi, Aditya Ni, Dong Yang, Xin Zhou, Guang-Quan Wang, Kaini Heller, Nicholas Papanikolopoulos, Nikolaos Weight, Christopher Tong, Yubing Udupa, Jayaram K. Patrick, Cahill J. Wang, Yaqi Zhang, Yifan Contijoch, Francisco McVeigh, Elliot Ye, Xin He, Shucheng Haase, Robert Pinetz, Thomas Radbruch, Alexander Krause, Inga Kobler, Erich He, Jian Tang, Yucheng Yang, Haichun Huo, Yuankai Luo, Gongning Kushibar, Kaisar Amankulov, Jandos Toleshbayev, Dias Mukhamejan, Amangeldi Egger, Jan Pepe, Antonio Gsaxner, Christina Luijten, Gijs Fujita, Shohei Kikuchi, Tomohiro Wiestler, Benedikt Kirschke, Jan S. de la Rosa, Ezequiel Bolelli, Federico Lumetti, Luca Grana, Costantino Xie, Kunpeng Wu, Guomin Puladi, Behrus Martín-Isla, Carlos Lekadir, Karim Campello, Victor M. Shao, Wei Brisbane, Wayne Jiang, Hongxu Wei, Hao Yuan, Wu Li, Shuangle Zhou, Yuyin Wang, Bo AI Collaborative Centre University Health Network Department of Laboratory Medicine and Pathobiology University of Toronto Vector Institute Toronto Canada Peter Munk Cardiac Centre University Health Network Toronto Canada Toronto General Hospital Research Institute University Health Network Department of Computer Science University of Toronto University Health Network Vector Institute Toronto Canada University of Science Vietnam National University Ho Chi Minh City Viet Nam Institute of Computer Science University of Freiburg Freiburg Germany School of Medicine and Health Harbin Institute of Technology Harbin China Division of Image Processing Department of Radiology Leiden University Medical Center Leiden Netherlands Department of System and Control Engineering School of Engineering Institute of Science Tokyo Formerly Tokyo Institute of Technology Tokyo Japan Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Karlsruhe Germany School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China School of Electrical Engineering and Computer Science University of Queensland Brisbane Australia School of Cyberspace Hangzhou Dianzi University Hangzhou China Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Division for Computational Radiology and Clinical AI The Department of Neuroradiology University Hospital Bonn Germany Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany Division for Computational Radiology and Clinical AI Department of Neuroradiology University Hospital Bonn Germany School of Biomedical Engineering Shenzhen University Shenzhen China School of Biological Science and Medical Engineering Southeast University Nanjing China Department of Urology Cleveland Clinic Cleveland United States Department of Computer Science University of Minnesota Minneapolis United St

Promptable segmentation foundation models have emerged as a transformative approach to addressing the diverse needs in medical images, but most existing models require expensive computing, posing a big barrier to their adoption in clinical practice. In this work, we organized the first international competition dedicated to promptable medical image segmentation, featuring a large-scale dataset spanning nine common imaging modalities from over 20 different institutions. The top teams developed lightweight segmentation foundation models and implemented an efficient inference pipeline that substantially reduced computational requirements while maintaining state-of-the-art segmentation accuracy. Moreover, the post-challenge phase advanced the algorithms through the design of performance booster and reproducibility tasks, resulting in improved algorithms and validated reproducibility of the winning solution. Furthermore, the best-performing algorithms have been incorporated into the open-source software with a user-friendly interface to facilitate clinical adoption. The data and code are publicly available to foster the further development of medical image segmentation foundation models and pave the way for impactful real-world applications. © 2024, CC BY-NC-SA.

关键词： Open source software

Observability for port-Hamiltonian systems

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arXiv 2021年

作者： Jacob, Birgit Zwart, Hans School of Mathematics and Natural Sciences University of Wuppertal Imacm Gaußstraße 20 WuppertalD-42119 Germany Department of Mechanical Engineering Dynamics and Control Group Eindhoven University of Technology Eindhoven5612 AZ Netherlands Department of Applied Mathematics Mathematics and Computer Science Faculty of Electrical Engineering University of Twente Enschede7500 AE Netherlands

The class of port-Hamiltonian systems incorporates many physical models, such as mechanical systems in the finite-dimensional case and wave and beam equations in the infinite-dimensional case. In this paper we study a subclass of linear first order port-Hamiltonian systems. In [3], it is shown that these systems are exactly observable when the energy is not dissipated internally and when sufficient observations are made at the boundary. In this article we study the observability properties for these systems when internal dissipation of energy is possible. We cannot show the exact observability, but we do show that the Hautus test is satisfied. In general, the Hautus test is weaker than exact observability, but stronger than approximate observability. Hence we conclude that these systems are approximately observable. © 2021, CC BY.

关键词： Hamiltonians

Optimal control of Joint Multi-Virus Infection and Information Spreading

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IFAC-PapersOnLine 2020年第2期53卷 6650-6655页

作者： Vladislav Taynitskiy Elena Gubar Denis Fedyanin Ilya Petrov Quanyan Zhu St. Petersburg State University Faculty of Applied Mathematics and Control Processes Saint-Petersburg Russia V.A. Trapeznikov Institute of Control Sciences of RAS Moscow Russia Department of Electrical and Computer Engineering New York University USA

Nowadays, epidemic models provide an appropriate tool to describe the propagation of biological viruses in human or animal populations, rumors and misinformation in social networks, and malware in both computer and ad hoc networks. It is common that there are multiple types of malware infecting a network of computing devices, and different messages can spread over the social network. Information spreading and virus propagation are interdependent processes. To capture their independencies, we integrate two epidemic models into one holistic framework, known as the modified Susceptible-Warned-Infected-Recovered-Susceptible (SWIRS) model. The first epidemic model describes the information spreading regarding the risk of malware attacks and possible preventive procedures. The second one describes the propagation of multiple viruses over the network of devices. To minimize the impact of the virus spreading and improve the protection of the networks, we consider an optimal control problem with two types of control strategies: information spreading among healthy nodes and the treatment of infected nodes. We obtain the structure of optimal control strategies and study the condition of epidemic outbreaks. The main results are extended to the case of the network of two connected clusters. Numerical examples are used to corroborate the theoretical findings.

关键词： Network Security Optimal control Epidemic Process Information Spreading

A Non-negative Matrix Factorization Based Method for Identifying Essential Proteins

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Research Square

Research Square 2021年

作者： Zhang, Zhihong Hu, Sai Yan, Wei Zhao, Bihai Wang, Lei School of Information Technology and Management Hunan University of Finance and Economics Changsha410205 China College of Computer Engineering and Applied Mathematics Changsha University Hunan Changsha410022 China Hunan Provincial Key Laboratory of Nutrition and Quality Control of AquaticAnimals Department of Biological and Environmental Engineering Changsha University Hunan Changsha410022 China

Background Identification of essential proteins is very important for understanding the basic requirements to sustain a living organism. In recent years, various different computational methods have been proposed to identify essential proteins based on protein-protein interaction (PPI) networks. However, there has been reliable evidence that a huge amount of false negatives and false positives exist in PPI data. Therefore, it is necessary to reduce the influence of false data on accuracy of essential proteins prediction by integrating multi-source biological information with PPI networks. Results In this paper, we proposed a non-negative matrix factorization and multiple biological information based model (NDM) for identifying essential proteins. The first stage in this progress was to construct a weighted PPI network by combing the information of protein domain, protein complex and the topology characteristic of the original PPI network. Then, the non-negative matrix factorization technique was used to reconstruct an optimized PPI network with whole enough weight of edges. In the final stage, the ranking score of each protein was computed by the PageRank algorithm in which the initial scores were calculated with homologous and subcellular localization information. In order to verify the effectiveness of the NDM method, we compared the NDM with other state-of-the-art essential proteins prediction methods. The comparison of the results obtained from different methods indicated that our NDM model has better performance in predicting essential proteins. Conclusion Employing the non-negative matrix factorization and integrating multi-source biological data can effectively improve quality of the PPI network, which resulted in the led to optimization of the performance essential proteins identification. This will also provide a new perspective for other prediction based on protein-protein interaction networks. © 2021, CC BY.

关键词： Non-negative matrix factorization

Lecture Notes in Networks and Systems

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Preface

Lecture Notes in Networks and Systems 2024年 1076 LNNS卷 v-vii页

作者： Biele, Cezary Kacprzyk, Janusz Kopeć, Wieslaw Możaryn, Jakub Owsiński, Jan W. Romanowski, Andrzej Sikorski, Marcin National Information Processing Institute Warszaw Poland Polish Academy of Sciences Systems Research Institute Warsaw Poland Polish-Japanese Academy of Information Technology Warsaw Poland Institute of Automatic Control and Robotics Faculty of Mechatronics Warsaw University of Technology Warsaw Poland Institute of Applied Computer Science Faculty of Electrical Electronic Computer and Control Engineering Lódż University of Technology LODZ Poland Department of Applied Informatics in Management Faculty of Management and Economics Gdańsk University of Technology Gdańsk Poland

Eight Become One: the EURECA-PRO University Network

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BHM Berg- und Huttenmannische Monatshefte 2022年第10期167卷 462-471页

作者： Kircher, Volkmar Feiel, Susanne Bongaerts, Jan C. Sishchuk, Julia Drebenstedt, Carsten Lagoudakis, Michail Fragkedaki, Konstantina García-Llamas, Paula Suárez-Corona, Adriana Fernández-Raga, María Baelo-Álvarez, Roberto Górski, Marcin Pawelczyk, Marek Hochstein, Synnöve Walter, Stefanie Speck, Maria Ravas, Oana Van Hellemont, Elke Vancauwenbergh, Sadia Resources Innovation Center Leoben Montanuniversität Leoben Leoben Austria Public Relations Office International Relations and University Cooperation Montanuniversität Leoben Leoben Austria Faculty of Business Administration Technische Universität Bergakademie Freiberg Freiberg Germany EURECA-PRO Head of work package education and studies Technische Universität Bergakademie Freiberg Freiberg Germany EURECA-PRO Head of educational council Technische Universität Bergakademie Freiberg Freiberg Germany School of Electrical and Computer Engineering Technical University of Crete Chania Greece EURECA-PRO European University Technical University of Crete Chania Greece Department of General and Specific Didactics and Educational Theory University of León Leon24071 Spain Department of Mathematical Sciences Universidad de León León24071 Spain Department of Applied Physics University of León León24071 Spain Faculty of Civil Engineering Silesian University of Technology Gliwice Poland Department of Measurements and Control Systems Silesian University of Technology Gliwice Poland Fakultät Medien Hochschule Mittweida Mittweida Germany International Relation Office University of Petroşani Petroşani Romania Rector’s Office Hasselt University Hasselt Belgium Directorate Research Library and Internationalisation Hasselt University Hasselt Belgium

The United Nations Sustainable Development Goal 12 is the foundation of the European University on Responsible Consumption and Production consortium (EURECA-PRO). This article introduces the eight EURECA-PRO partner universities: Montanuniversität Leoben (Austria), Technische Universität Bergakademie Freiberg (Germany), Technical University of Crete (Greece), University of León (Spain), Silesian University of Technology (Poland), Mittweida University of applied Sciences (Germany), University of Petroşani (Romania), and Hasselt University (Belgium). In addition, each university’s role within the alliance and unique research and study programmes are outlined. The synergy created by EURECA-PRO enables the pursuit of an ambitious research agenda with five research "Lighthouse Missions" as well as the implementation of joint study programmes. © 2022, The Author(s).

关键词： Sustainable development

A stochastic perturbed augmented Lagrangian method for smooth convex constrained minimization

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arXiv 2025年

作者： Singh, Nitesh Kumar Necoara, Ion School of Computer Science University of Petroleum and Energy Studies Uttarakhand Dehradun248007 India Automatic Control and Systems Engineering Department University Politehnica Bucharest Bucharest060042 Romania Gheorghe Mihoc-Caius Iacob Institute of Mathematical Statistics and Applied Mathematics of the Romanian Academy Bucharest050711 Romania

This paper considers smooth convex optimization problems with many functional constraints. To solve this general class of problems we propose a new stochastic perturbed augmented Lagrangian method, called SGDPA, where a perturbation is introduced in the augmented Lagrangian function by multiplying the dual variables with a subunitary parameter. Essentially, we linearize the objective and one randomly chosen functional constraint within the perturbed augmented Lagrangian at the current iterate and add a quadratic regularization that leads to a stochastic gradient descent update for the primal variables, followed by a perturbed random coordinate ascent step to update the dual variables. We provide a convergence analysis in both optimality and feasibility criteria for the iterates of SGDPA algorithm using basic assumptions on the problem. In particular, when the dual updates are assumed to be bounded, we prove sublinear rates of convergence for the iterates of algorithm SGDPA of order O(k−1/2) when the objective is convex and of order O(k−1) when the objective is strongly convex, where k is the iteration counter. Under some additional assumptions, we prove that the dual iterates are bounded and in this case we obtain convergence rates of order O(k−1/4) and O(k−1/2) when the objective is convex and strongly convex, respectively. Preliminary numerical experiments on problems with many quadratic constraints demonstrate the viability and performance of our method when compared to some existing state-of-the-art optimization methods and software. © 2025, CC BY.

关键词： Lagrange multipliers

Automated osteoporosis classification and T-score prediction using hip radiographs via deep learning algorithm

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Therapeutic Advances in Musculoskeletal Disease 2024年 16卷

作者： Chen, Yu-Pin Chan, Wing P. Zhang, Han-Wei Tsai, Zhi-Ren Peng, Hsiao-Ching Huang, Shu-Wei Jang, Yeu-Chai Kuo, Yi-Jie Department of Orthopedics Wan Fang Hospital Taipei Medical University Taipei City Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan Department of Radiology Wan Fang Hospital Taipei Medical University Taipei City Taiwan Department of Radiology School of Medicine College of Medicine Taipei Medical University Taipei City Taiwan Biomedica Corporation New Taipei City Taiwan Program for Aging China Medical University Taichung City Taiwan Institute of Population Health Sciences National Health Research Institutes Miaoli County Taiwan Department of Electrical and Computer Engineering Institute of Electrical Control Engineering National Yang Ming Chiao Tung University Hsinchu County Hsinchu City Taiwan Department of Computer Science and Information Engineering Asia University Taichung City Taiwan Department of Medical Research China Medical University Hospital China Medical University Taichung City Taiwan Center for Precision Medicine Research Asia University Taichung City Taiwan Department of Applied Science National Taitung University Taitung County Taitung City Taiwan Department of Orthopedics Wan Fang Hospital Taipei Medical University No. 111 Sec. 3 Xinglong Road Wenshan Taipei 11696 Taiwan Department of Orthopedics School of Medicine College of Medicine Taipei Medical University Taipei Taiwan

Background: Despite being the gold standard for diagnosing osteoporosis, dual-energy X-ray absorptiometry (DXA) is an underutilized screening tool for osteoporosis. Objectives: This study proposed and validated a controllable feature layer of a convolutional neural network (CNN) model with a preprocessing image algorithm to classify osteoporosis and predict T-score on the proximal hip region via simple hip radiographs. Design: This was a single-center, retrospective study. Methods: An image dataset of 3460 unilateral hip images from 1730 patients (age ⩾50 years) was retrospectively collected with matched DXA assessment for T-score for the targeted proximal hip regions to train (2473 unilateral hip images from 1430 patients) and test (497 unilateral hip images from 300 patients) the proposed CNN model. All images were processed with a fully automated CNN model, X1AI-Osteo. Results: The proposed screening tool illustrated a better performance (sensitivity: 97.2%;specificity: 95.6%;positive predictive value: 95.7%;negative predictive value: 97.1%;area under the curve: 0.96) than the open-sourced CNN models in predicting osteoporosis. Moreover, when combining variables, including age, body mass index, and sex as features in the training metric, there was high consistency in the T-score on the targeted hip regions between the proposed CNN model and the DXA (r = 0.996, p < 0.001). Conclusion: The proposed CNN model may identify osteoporosis and predict T-scores on the targeted hip regions from simple hip radiographs with high accuracy, highlighting the future application for population-based opportunistic osteoporosis screening with low cost and high adaptability for a broader population at risk. Trial registration: TMU-JIRB N201909036. © The Author(s), 2024.

关键词： deep learning neural network osteoporosis radiographs T-score