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Maintenance of smart buildings using fault trees

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arXiv 2018年

作者： Cauchi, Nathalie Hoque, Khaza Anuarul Stoelinga, Marielle Abate, Alessandro Department of Computer Science University of Oxford Formal Methods and Tools Group University of Twente Netherlands Department of Software Science Radboud University Netherlands

Timely maintenance is an important means of increasing system dependability and life span. Fault Maintenance trees (FMTs) are an innovative framework incorporating both maintenance strategies and degradation models and serve as a good planning platform for balancing total costs (operational and maintenance) with dependability of a system. In this work, we apply the FMT formalism to a Smart Building application and propose a framework that efficiently encodes the FMT into Continuous Time Markov Chains. This allows us to obtain system dependability metrics such as system reliability and mean time to failure, as well as costs of maintenance and failures over time, for different maintenance policies. We illustrate the pertinence of our approach by evaluating various dependability metrics and maintenance strategies of a Heating, Ventilation and Air-Conditioning system. Copyright © 2018, The Authors. All rights reserved.

关键词： Reliability

Association Rules Transformation for Knowledge Integration and Warehousing 17th

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Association Rules Transformation for Knowledge Integration a...

17th International Conference on Intelligent Systems Design and Applications, ISDA 2017

作者： Ayadi, Rim Hachaichi, Yasser Feki, Jamel Multimedia Information Systems and Advanced Computing Laboratory University of Sfax Sfax Tunisia Department of Computer Science and Quantitative Methods Higher Institute of Business Administration of Sfax Sfax Tunisia Faculty of Computing and IT University of Jeddah Jeddah Saudi Arabia

ISBN: (纸本)9783319763477

Knowledge management process is a set of procedures and tools applied to facilitate capturing, sharing and effectively using knowledge. However, knowledge collected from organizations is generally expressed in various formalisms, therefore it is heterogeneous. Thus, a Knowledge Warehouse (KW), which is a solution for implementing all phases of the knowledge management process, should solve this structural heterogeneity before loading and storing knowledge. In this paper, we are interested in knowledge normalization. More accurately, we firstly introduce our proposed architecture for a KW, and then we present the MOT (Modeling with Object Types) language for knowledge representation. Since our objective is to transform heterogeneous knowledge into MOT, as a pivot model, we suggest a meta-model for the MOT and another for the explicit knowledge extracted through the association rules technique. Thereafter, we define eight transformation rules and an algorithm to transform an association rules model into the MOT model. © 2018, Springer International Publishing AG, part of Springer Nature.

关键词： Data mining

Bell inequalities for S4 group: classical and quantum bounds for two orbits case

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arXiv 2019年

作者： Bolonek-Lasoń, Katarzyna Kosiński, Piotr Department of Statistical Methods Faculty of Economics and Sociology University of Lodz 41/43 Rewolucji 1905 St. Lodz90-214 Poland Department of Computer Science Faculty of Physics and Applied Informatics University of Lodz 149/153 Pomorska St. Lodz90-236 Poland

Within Güney-Hillery approach a number of examples of classical and quantum bounds on sum of probabilities resulting from two orbits of S4 is considered. It is shown that the violation of Bell’s inequalities is ...

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Classical and quantum speed limits

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arXiv 2019年

作者： Bolonek-Lasoń, Katarzyna Gonera, Joanna Kosiński, Piotr Department of Statistical Methods Faculty of Economics and Sociology University of Lodz 41/43 Rewolucji 1905 St. Lodz90-214 Poland Department of Computer Science Faculty of Physics and Applied Informatics University of Lodz 149/153 Pomorska St. Lodz90-236 Poland

The new bound on quantum speed limit (in terms of relative purity) is derived by applying the original Mandelstam-Tamm one to the evolution in the space of Hilbert-Schmidt operators acting in the initial space of states. It is shown that it provides the quantum counterpart of the classical speed limit derived in Phys. Rev. Lett. 120 (2018), 070402 and the ~ → 0 limit of the former yields the latter. The existence of classical limit is related to the degree of mixing of the quantum state. © 2019, CC BY.

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Improving Undergraduate Astronomy Students’ Skills with Research Literature via Accessible Summaries: An Exploratory Case Study with Astrobites-based Reading Assignments

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arXiv 2023年

作者： Lewis, Briley L. Waggoner, Abygail R. Clarke, Emma Crisp, Alison L. Dodici, Mark Doskoch, Graham M. Foley, Michael M. Golant, Ryan Grayson, Skylar Hegde, Sahil Cuestas, Nathalie Korhonen Law, Charles J. Lefever, R.R. Mishra, Ishan Popinchalk, Mark Sagynbayeva, Sabina Wong, Samantha L. Yan, Wei Ingraham Dixie, Kaitlyn L. Supriya, K. Department of Physics University of California Santa Barbara Santa BarbaraCA93108 United States Department of Physics and Astronomy University of California Los Angeles Los AngelesCA90095 United States University of Virginia CharlottesvilleVA22904 United States University of Wisconsin MadisonWI53706 United States McWilliams Center for Comsology Department of Physics Carnegie Mellon University PittsburghPA15213 United States Department of Astronomy The Ohio State University ColumbusOH43210 United States David A. Dunlap Department of Astronomy & Astrophysics University of Toronto TorontoONM5S 3H4 Canada Department of Physics and Astronomy West Virginia University MorgantownWV26506 United States Center for Astrophysics — Harvard & Smithsonian CambridgeMA0213 United States Department of Earth and Planetary Sciences Harvard University CambridgeMA02138 United States Department of Astronomy Columbia University New YorkNY10027 United States School of Earth and Space Exploration Arizona State University TempeAZ85281 United States Department of Physics and Astronomy Northwestern University 2145 Sheridan Road EvanstonIL60208 United States Northwestern University 1800 Sherman Avenue EvanstonIL60201 United States Department of Astronomy University of Virginia CharlottesvilleVA22904 United States Zentrum für Astronomie der Universität Heidelberg Astronomisches Rechen-Institut Heidelberg69120 Germany Jet Propulsion Laboratory California Institute of Technology La Cañada FlintridgeCA91011 United States Department of Astrophysics American Museum of Natural History New YorkNY10024 United States Department of Physics and Astronomy Stony Brook University Stony BrookNY11794 United States Center for Computational Astrophysics Flatiron Institute New YorkNY10010 United States Physics Department McGill University MontrealQCH3A 2T8 Canada Department of Mathematics Computer Science and Physics Wartburg College WaverlyIA50677 Un

Undergraduate physics and astronomy students are expected to engage with scientific literature as they begin their research careers, but reading comprehension skills are rarely explicitly taught in major courses. We seek to determine the efficacy of a reading assignment designed to improve undergraduate astronomy (or related) majors’ perceived ability to engage with research literature by using accessible summaries of current research written by experts in the field. During the 2022-2023 academic year, faculty members from six institutions incorporated reading assignments using accessible summaries from Astrobites into their undergraduate astronomy major courses, surveyed their students before and after the activities, and participated in follow-up interviews with our research team. Quantitative and qualitative survey data from 52 students show that students’ perceptions of their abilities with jargon and identifying main takeaways of a paper significantly improved with use of the tested assignment template. Additionally, students report increased confidence of their abilities within astronomy after exposure to these assignments, and instructors valued a ready-to-use resource to incorporate reading comprehension in their pedagogy. This exploratory case study with Astrobites-based assignments suggests that incorporating current research in the undergraduate classroom through accessible literature summaries may increase students’ confidence and ability to engage with research literature, assisting in their preparation for participation in research careers. © 2023, CC BY.

关键词： Students

Empirical evaluation of educational interactive systems

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Quality & Quantity 2018年第6期52卷 2427-2434页

作者： García-Peñalvo, Francisco José Moreno López, Lourdes Sánchez-Gómez, Mª Cruz Computer Science Department Research Institute for Educational Sciences GRIAL Research Group University of Salamanca Salamanca Spain Computer Science Department Universidad Carlos III de Madrid Madrid Spain Didactics Organization and Research Methods Department Faculty of Education University of Salamanca Salamanca Spain

Educational software systems are usually highly interactive systems that needs to be evaluated from two different perspectives, first as interactive software, mainly using usability and accessibility approaches, and second as an educational product that needs to be evaluated from its learning impact. The empirical evaluation methods may be quantitative, with a more deductive reasoning, or qualitative, with a more inductive reasoning, oriented, however mixed approaches have been used extensively because they combine the both main methods with a more pragmatic knowledge focusing on knowing the consequences or solving problems. The empirical evaluation of interactive educational software due to its own characteristics requires a mixed approach typically. This special issue is devoted to identify, share and valorise best practices and experiences, with a very solid modelling background, that are focused on the empirical evaluation of educational interactive systems with a special emphasis on mixed quantitative and qualitative approaches.

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Bayesian reliability analysis of exponential distribution model under a new loss function

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International Journal of Performability Engineering 2018年第8期14卷 1815-1823页

作者： Ren, Haiping Chao, Shigang Teaching Department of Basic Subjects Jiangxi University of Science and Technology Nanchang330013 China School of Mathematics and Computer Science Yichun University Yichun336000 China

Loss function is an important content in Bayes statistical inference. The task of this article is to study the reliability analysis of the exponential model based on a new proposed symmetric loss function. The new proposed loss function is established on the basis of the LINEX asymmetric loss function. Firstly, the Bayes estimation of the parameter is derived under the prior distribution of the parameter based on non-information Quasi prior distribution, and then the admissibility of the estimators are also discussed. Furthermore, this paper puts forward a novel testing procedure to evaluate the lifetime performance of exponential products based on the new derived Bayes estimator. Finally, Monte Carlo statistical simulation and an applicable example are used to illustrate that the new proposed Bayes estimators and testing procedure are effective and feasible. © 2018 Totem Publisher, Inc. All rights reserved.

关键词： Reliability analysis

Bilevel Optimization Based on Kriging Approximations of Lower Level Optimal Value Function

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Bilevel Optimization Based on Kriging Approximations of Lowe...

Congress on Evolutionary Computation

作者： Ankur Sinha Samish Bedi Kalyanmoy Deb Production and Quantitative Methods Indian Institute of Management Ahmedabad India Department of Computer Science Birla Institute of Technology and Science Pilani India Department of Electrical and Computer Engineering Michigan State University MI USA

A large number of application problems involve two levels of optimization, where one optimization task is nested inside the other. These problems are known as bilevel optimization problems and have been widely studied by researchers in the area of mathematical optimization. Bilevel optimization problems are known to be difficult and computationally demanding. Most of the solution procedures proposed until now are either computationally very expensive or applicable to only a narrow class of bilevel optimization problems involving small number of variables. In this paper, we propose a global optimization algorithm for bilevel optimization using Kriging approximation based model that tries to reduce the computational expense by iteratively approximating an important mapping in bilevel optimization; namely, the lower level optimal value function mapping. The lower level optimal value function is useful in reducing the two level optimization task to one; however, identifying this function is not straightforward. Our approach aims at meta-modeling this mapping and solving a number of auxiliary single level problems to arrive at the bilevel optimum. In our study, we test the methodology on a number of test problems. The preliminary results are quite promising which suggest the viability of the approach in solving more complicated bilevel test problem. To the best knowledge of the authors, such kind of a solution procedure based on iterative approximation of the optimal lower level value function using a stochastic process has not been widely used in bilevel optimization.

关键词： Optimization Task analysis Linear programming Evolutionary computation Government Computational modeling Standards

Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge

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arXiv 2024年

作者： LaBella, Dominic Baid, Ujjwal Khanna, Omaditya McBurney-Lin, Shan McLean, Ryan Nedelec, Pierre Rashid, Arif Tahon, Nourel Hoda Altes, Talissa Bhalerao, Radhika Dhemesh, Yaseen Godfrey, Devon Hilal, Fathi Floyd, Scott Janas, Anastasia Kazerooni, Anahita Fathi Kirkpatrick, John Kent, Collin Kofler, Florian Leu, Kevin Maleki, Nazanin Menze, Bjoern Pajot, Maxence Reitman, Zachary J. Rudie, Jeffrey D. Saluja, Rachit Velichko, Yury Wang, Chunhao Warman, Pranav Adewole, Maruf Albrecht, Jake Anazodo, Udunna Anwar, Syed Muhammad Bergquist, Timothy Chen, Sully Francis Chung, Verena Chai, Rong Conte, Gian-Marco Dako, Farouk Eddy, James Ezhov, Ivan Khalili, Nastaran Iglesias, Juan Eugenio Jiang, Zhifan Johanson, Elaine Van Leemput, Koen Li, Hongwei Bran Linguraru, Marius George Liu, Xinyang Mahtabfar, Aria Meier, Zeke Moawad, Ahmed W. Mongan, John Piraud, Marie Shinohara, Russell Takeshi Wiggins, Walter F. Abayazeed, Aly H. Akinola, Rachel Jakab, András Bilello, Michel de Verdier, Maria Correia Crivellaro, Priscila Davatzikos, Christos Farahani, Keyvan Freymann, John Hess, Christopher Huang, Raymond Lohmann, Philipp Moassefi, Mana Pease, Matthew W. Vollmuth, Phillipp Sollmann, Nico Diffley, David Nandolia, Khanak K. Warren, Daniel I. Hussain, Ali Fehringer, Pascal Bronstein, Yulia Deptula, Lisa Stein, Evan G. Taherzadeh, Mahsa de Oliveira, Eduardo Portela Haughey, Aoife Kontzialis, Marinos Saba, Luca Turner, Benjamin Brüßeler, Melanie M.T. Ansari, Shehbaz Gkampenis, Athanasios Weiss, David Maximilian Mansour, Aya Shawali, Islam H. Yordanov, Nikolay Stein, Joel M. Hourani, Roula Moshebah, Mohammed Yahya Abouelatta, Ahmed Magdy Rizvi, Tanvir Willms, Klara Martin, Dann C. Okar, Abdullah D’Anna, Gennaro Taha, Ahmed Sharifi, Yasaman Faghani, Shahriar Kite, Dominic Pinho, Marco Haider, Muhammad Ammar Aristizabal, Alejandro Karargyris, Alexandros Kassem, Hasan Pati, Sarthak Sheller, Micah Alonso-Basanta, Michelle Villanueva-Meyer, Javier Rauschecker, Andreas M. Nada, Ayman Aboian, Mariam Flanders, Adam E. Wiestler, Benedikt Bakas, Duke University Medical Center Department of Radiation Oncology DurhamNC United States Division of Computational Pathology Department of Pathology and Laboratory Medicine Indiana University School of Medicine IndianapolisIN United States Center for Federated Learning in Medicine Indiana University IndianapolisIN United States Department of Neurosurgery Thomas Jefferson University PhiladelphiaPA United States University of California San FranciscoCA United States Yale University New HavenCT United States Department of Radiation Oncology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University of Missouri ColumbiaMO United States Children’s Hospital of Philadelphia University of Pennsylvania PhiladelphiaPA United States University of Pennsylvania PhiladelphiaPA United States Helmholtz AI Helmholtz Munich Germany Department of Informatics Technical University Munich Germany TranslaTUM - Central Institute for Translational Cancer Research Technical University of Munich Germany Department of Diagnostic and Interventional Neuroradiology School of Medicine Klinikum rechts der Isar Technical University of Munich Germany San FranciscoCA United States Biomedical Image Analysis and Machine Learning Department of Quantitative Biomedicine University of Zurich Switzerland Department of Neuroradiology Technical University of Munich Munich Germany University of San Diego CA United States Cornell University IthacaNY United States Department of Radiology Northwestern University EvanstonIL United States Duke University Medical Center School of Medicine DurhamNC United States Lab Crestview Radiology Lagos Nigeria Sage Bionetworks United States McGill University MontrealQC Canada Children’s National Hospital WashingtonDC United States George Washington University WashingtonDC United States Mayo Clinic RochesterMN United States Center for Global Health Perelman School of Medicine University of Pennsylvani

We describe the design and results from the BraTS 2023 Intracranial Meningioma Segmentation Challenge. The BraTS Meningioma Challenge differed from prior BraTS Glioma challenges in that it focused on meningiomas, which are typically benign extra-axial tumors with diverse radiologic and anatomical presentation and a propensity for multiplicity. Nine participating teams each developed deep-learning automated segmentation models using image data from the largest multi-institutional systematically expert annotated multilabel multi-sequence meningioma MRI dataset to date, which included 1000 training set cases, 141 validation set cases, and 283 hidden test set cases. Each case included T2, FLAIR, T1, and T1Gd brain MRI sequences with associated tumor compartment labels delineating enhancing tumor, non-enhancing tumor, and surrounding non-enhancing FLAIR hyperintensity. Participant automated segmentation models were evaluated and ranked based on a scoring system evaluating lesion-wise metrics including dice similarity coefficient (DSC) and 95% Hausdorff Distance. The top ranked team had a lesion-wise median dice similarity coefficient (DSC) of 0.976, 0.976, and 0.964 for enhancing tumor, tumor core, and whole tumor, respectively and a corresponding average DSC of 0.899, 0.904, and 0.871, respectively. These results serve as state-of-the-art benchmarks for future pre-operative meningioma automated segmentation algorithms. Additionally, we found that 1286 of 1424 cases (90.3%) had at least 1 compartment voxel abutting the edge of the skull-stripped image edge, which requires further investigation into optimal pre-processing face anonymization steps. © 2024, CC BY.

关键词： Deep learning