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arXiv 2018年

作者： Singla, Abhik Bhattacharya, Shounak Dholakiya, Dhaivat Bhatnagar, Shalabh Ghosal, Ashitava Amrutur, Bharadwaj Kolathaya, Shishir Faculty of Mechanical Engineering Faculty of Electrical & Computer Engineering Robert Bosch Center for Cyber Physical Systems IISc Bengaluru India Faculty of Computer Science and Automation

Humans and animals are believed to use a very minimal set of trajectories to perform a wide variety of tasks including walking. Our main objective in this paper is two fold 1) Obtain an effective tool to realize these basic motion patterns for quadrupedal walking, called the kinematic motion primitives (kMPs), via trajectories learned from deep reinforcement learning (D-RL) and 2) Realize a set of behaviors, namely trot, walk, gallop and bound from these kinematic motion primitives in our custom four legged robot, called the "Stoch". D-RL is a data driven approach, which has been shown to be very effective for realizing all kinds of robust locomotion behaviors, both in simulation and in experiment. On the other hand, kMPs are known to capture the underlying structure of walking and yield a set of derived behaviors. We first generate walking gaits from D-RL, which uses policy gradient based approaches. We then analyze the resulting walking by using principal component analysis. We observe that the kMPs extracted from PCA followed a similar pattern irrespective of the type of gaits generated. Leveraging on this underlying structure, we then realize walking in Stoch by a straightforward reconstruction of joint trajectories from kMPs. This type of methodology improves the transferability of these gaits to real hardware, lowers the computational overhead on-board, and also avoids multiple training iterations by generating a set of derived behaviors from a single learned gait. Copyright © 2018, The Authors. All rights reserved.

关键词： Kinematics

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Understanding metric-related pitfalls in image analysis validation

arXiv

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision

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Hierarchic neighbors embedding

arXiv

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arXiv 2019年

作者： Liu, Shenglan Yu, Yang Liu, Yang Qiao, Hong Feng, Lin Feng, Jiashi School of Computer Science and Technology Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian Liaoning116024 China State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Department of Electrical and Computer Engineering National University of Singapore Singapore Singapore

Manifold learning now plays a very important role in machine learning and many relevant applications. Although its superior performance in dealing with nonlinear data distribution, data sparsity is always a thorny knot. There are few researches to well handle it in manifold learning. In this paper, we propose Hierarchic Neighbors Embedding (HNE), which enhance local connection by the hierarchic combination of neighbors. After further analyzing topological connection and reconstruction performance, three different versions of HNE are given. The experimental results show that our methods work well on both synthetic data and high-dimensional real-world tasks. HNE develops the outstanding advantages in dealing with general data. Furthermore, comparing with other popular manifold learning methods, the performance on sparse samples and weak-connected manifolds is better for HNE. Copyright © 2019, The Authors. All rights reserved.

关键词： Topology

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Research on temperature cascade control of distillation column based on Smith-Fuzzy-PID

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Paper Asia 2018年第COMPENDIUM 2期 91-94页

作者： Wang, Yeqin Chen, Yunhao Liu, Qihui Zhang, Yue Faculty of Automation Huaiyin Institute of Technology Huaian China Faculty of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

The efficiency and quality of oil recovery depend largely on the temperature control in distillation column, a Smith-Fuzzy-PID cascade control system for the distillation column proposed to overcome the drawback of single loop control in terms of rapidity and stability. Fuzzy-PID controller is designed in vice loop to achieve the coarse control by controlling the material flow. The Smith predictor was introduced into the feedback loop of the main loop to solve the time-delay in the traditional PID control. The simulated results show that the proposed control system has good characteristic in terms of dynamic and steady-state performance and robustness. © 2018 SHPMedia Sdn Bhd. All rights reserved.

关键词： Distillation columns

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Comparison of the PLL Control techniques applied in Photovoltaic System

Comparison of the PLL Control techniques applied in Photovol...

引用

Brazilian Power Electronics Conference, COBEP

作者： Marenice M. de Carvalho Renan L. P. Medeiros Iury V. Bessa Florindo A. C. Junior Kevin E. Lucas David A. Vaca Department of Electricity Federal University of Amazonas (UFAM) Manaus Brazil Department of Automation and Systems Federal University of Santa Catarina (UFSC) Florianopólis Brazil Faculty of Electrical and Computer Engineering ESPOL Polytechnic University Guayaquil Ecuador

ISBN: (数字)9781728141800

ISBN: (纸本)9781728141817

The increasing demand for low-carbon power and concerns about the environment resulted in the proliferation of alternative sources of energy. Among these sources, the photovoltaic systems stand out. However, these systems produce energy in direct current and require power inverters to connect them to the electric grid. For this connection to be realized the generated signal must have the same parameters of amplitude, phase, and frequency of the electrical network. In this sense, Phase-Locked Loop (PLL) controllers are used to synchronize the output signal with the network signal. For this type of system there are several topologies, such as PLL Synchronous Reference Frame (SRF-PLL), Second-Order Generalized Integrator PLL (SOGI-PLL), Enhanced PLL (EPLL) and Quadrature PLL (QPLL). This work proposes to compare these four controller topologies applied to the inverter of the photovoltaic system connected to the electric grid.

关键词： Phase locked loops Topology Power quality Harmonic analysis Photovoltaic systems Network topology Power harmonic filters

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Scheduling for Re-entrant Hybrid Flowshop Based on Wolf Pack Algorithm

Scheduling for Re-entrant Hybrid Flowshop Based on Wolf Pack...

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2018 International Conference on Advanced Materials, Intelligent Manufacturing and automation, AMIMA 2018

作者： Han, Zhonghua Tian, Xutian Ma, Xiaofu Lin, Shuo Faculty of Information and Control Engineering Shenyang Jianzhu University Liaoning110168 China Digital Factory Department Shenyang Institute of Automation CAS Shenyang110016 China Information and Control Engineering Faculty Shenyang Jianzhu University Liaoning110168 China Department of Electrical and Computer Engineering Virginia Tech BlacksburgVA24060 United States

In order to solve the scheduling problems of Re-entrant Hybrid Flowshop (RHFS), this paper investigates the mathematical programming model of RHFS, and proposes the wolf pack based algorithm (WPA) as a global optimization method. Multi-Sticking Crape Masking procedure scheduling problems in painting workshop of a bus manufacturing enterprise are of typical features of RHFS. We regard it as an application objective of the proposed algorithm, applying WPA and multiple evolutionary algorithms to solve the problems. The results show that the algorithm can solve re-entrant scheduling problems of hybrid flowshop more effectively compared with other conventional methods. © Published under licence by IOP Publishing Ltd.

关键词： Global optimization

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Machine learning identifies 6-gene signature in peripheral blood for pancreatic cancer diagnosis

引用

Heliyon 2025年第12期11卷

作者： Francisco Carrillo-Perez Octavio Caba Cristina Jiménez-Luna Francisco Ortuño Daniel Castillo-Secilla Luis Javier Herrera Jose Prados Ignacio Rojas Stanford Center for Biomedical Informatics Research (BMIR) Stanford University School of Medicine Stanford 94305-547 CA USA Institute of Biopathology and Regenerative Medicine (IBIMER) University of Granada Granada 18016 Granada Spain Biosanitary Institute of Granada (ibs.GRANADA) ibs.GRANADA Granada 18014 Granada Spain Department of Anatomy and Embryology Faculty of Medicine University of Granada Granada 18071 Granada Spain Department of Computer Engineering Automation and Robotics University of Granada Granada 18014 Granada Spain Computational Medicine Platform Andalusian Public Foundation Progress and Health-FPS Sevilla 41014 Sevilla Spain CoE Data Intelligence Fujitsu Technology Solutions S.A Madrid 28224 Madrid Spain

Pancreatic ductal adenocarcinoma (PDAC) is associated with a poor prognosis, primarily due to late-stage detection. This underscores the critical need for informative biomarkers enabling earlier diagnosis and improved patient outcomes. This study leveraged machine learning techniques to identify a biologically relevant gene signature for accurately differentiating PDAC, chronic pancreatitis (CP), and healthy controls using blood-based RNA sequencing data. We analyzed two distinct datasets: extracellular vesicle long RNA (exLR) and peripheral blood mononuclear cell (PBMC) RNA-Seq. Feature selection using the minimum Redundancy Maximum Relevance (mRMR) algorithm, followed by support vector machine (SVM) classification, identified a 15-gene signature derived from the exLR data. This signature successfully classified PDAC, CP, and healthy controls in both the exLR and PBMC datasets, achieving an F1-score of approximately 80 %. Further refinement yielded a 6-gene subset with established biological relevance to PDAC, which maintained strong classification performance (F1-score: 71.0 % in Leave-One-Out cross-validation). This study proposes a promising, biologically relevant gene signature derived from blood samples for the accurate, non-invasive differentiation of PDAC and CP, potentially facilitating earlier diagnosis and improving patient prognosis.

关键词： Machine learning Pancreatic ductal adenocarcinoma Chronic pancreatitis Peripheral blood mononuclear cells Clinical decision support systems Transcriptomics

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Temperature Dependent Dielectric Spectroscopy of Muscle Tissue Phantom

Temperature Dependent Dielectric Spectroscopy of Muscle Tiss...

引用

European Microwave Conference in Central Europe (EuMCE)

作者： Ondrej Fiser Sebastian Ley Marko Helbig Jürgen Sachs Michaela Kantova Jan Vrba Department of Biomedical Technology CTU Prague Czech Republic Faculty of Computer Science and Automation TU Ilmenau Germany Department of Electrical Engineering and Information Technology TU Ilmenau Germany Department of Electromagnetic Field CTU Prague Czech Republic

ISBN: (纸本)9781728112404;9782874870675

The temperature dependence of the dielectric parameters of tissues and tissue mimicking phantoms is very important for the non-invasive temperature measurement in medical applications using microwaves. We investigated and proposed a procedure of the measurement of this dependence in the temperature range 25 - 50 °C. Our proposed procedure was verified using distilled water. The results were compared to the literature model in the frequency range 150 - 3000 MHz. Using this method, the temperature dependence of dielectric parameters of new tissue mimicking phantom based on agar, PE powder and TX-151 was measured in the temperature range 25 -50 °C.

关键词： Temperature measurement Temperature dependence Dielectrics Calibration Phantoms Permittivity measurement

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Early detection of the advanced persistent threat attack using performance analysis of deep learning

arXiv

引用

arXiv 2020年

作者： Joloudari, Javad Hassannataj Haderbadi, Mojtaba Mashmool, Amir GhasemiGol, Mohammad Shahab, S. Mosavi, Amir Computer Engineering Department Faculty of Engineering University of Birjand Birjand Iran Department of Information Technology Mazandaran University of Science and Technology Babol Iran Institute of Research and Development Duy Tan University Da Nang550000 Viet Nam Future Technology Research Center College of Future National Yunlin University of Science and Technology 123 University Ro ad Section 3 Douliou Yunlin64002 Taiwan Institute of Automation Obuda University Budapest1034 Hungary School of Economics and Business Norwegian University of Life Sciences Ås1430 Norway Faculty of Civil Engineering Technische Universität Dresden Dresden01069 Germany Department of Informatics J. Selye University Komarno94501 Slovakia

One of the most common and important destructive attacks on the victim system is Advanced Persistent Threat (APT)-attack. The APT attacker can achieve his hostile goals by obtaining information and gaining financial benefits regarding the infrastructure of a network. One of the solutions to detect a secret APT attack is using network traffic. Due to the nature of the APT attack in terms of being on the network for a long time and the fact that the network may crash because of high traffic, it is difficult to detect this type of attack. Hence, in this study, machine learning methods such as C5.0 decision tree, Bayesian network and deep neural network are used for timely detection and classification of APT-attacks on the NSL-KDD dataset. Moreover, 10-fold cross validation method is used to experiment these models. As a result, the accuracy (ACC) of the C5.0 decision tree, Bayesian network and 6-layer deep learning models is obtained as 95.64%, 88.37% and 98.85%, respectively, and also, in terms of the important criterion of the false positive rate (FPR), the FPR value for the C5.0 decision tree, Bayesian network and 6-layer deep learning models is obtained as 2.56, 10.47 and 1.13, respectively. Other criterions such as sensitivity, specificity, accuracy, false negative rate and F-measure are also investigated for the models, and the experimental results show that the deep learning model with automatic multi-layered extraction of features has the best performance for timely detection of an APT-attack comparing to other classification models. Copyright © 2020, The Authors. All rights reserved.

关键词： Feature extraction

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Occupational health and safety risk management

Occupational health and safety risk management

引用

2018 Annual Session of Scientific Papers IMT ORADEA 2018

作者： Oarga, Ioan-Florin Rat Iu, Mariana Oarga, Ioan-Tudor Technical University of Cluj-Napoca Faculty of Machine Building Cluj-Napoca400641 Romania University of Oradea Department of Mechanical Engineering and Automotive Oradea410087 Romania Technical University of Cluj-Napoca Faculty of Automation and Computer Science Cluj-Napoca400027 Romania

The ongoing industrial revolution also includes the health and safety domain, which assists this development process. The participants and beneficiaries of this revolution don't have to endure a lowering of the protection levels from the beginning of it. Consequently, the health and safety domain needs to play its role in this historical stage. The risks of injury and illness are managed from the beginning, when the technological processes are being developed, on each production stage, until the end of every technological process. In order to achieve this, the professional risks must be identified, quantified, ranked and evaluated such that, the elaborated measures will eliminate or decrease the risks from the source. This control process of occupational health and safety risks of injury or illness uses documents which are applicable to any organization. These documents are legally required in each state, being instruments of the organization management, working like a system along with all other documents. © The Authors, published by EDP Sciences, 2018.

关键词： Risk management

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