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International Conference on Electrical and Electronics engineering, ELECO

作者： Alexis Polycarpou Sotiris Shiacallis Nicholas Christofides Antonis Papadakis Theofilos Papadopoulos Oguzhan Ceylan Department of Electrical Computer Engineering and Informatics Frederick Research Center Frederick University Nicosia Cyprus T.P. Aeolian Dynamics Ltd Cyprus Kyamos Multiphysics Nicosia Cyprus Electrical and Computer Engineering Department Democritus University of Thrace Deparment of Management Information Systems Kadir Has University Istanbul Turkey

Based on the achievements regarding the 2020 targets, the Renewable Energy Directive established a new, higher target for 2030, reflecting a significant increase of interest for renewable energy sources (RES). As a result, various new software simulation packages, featuring models of RES, have been developed and being used in academia as well as in industry for research, educational or training purposes. In this paper Wind turbine (WT) characteristics and wind speed data obtained from a commissioned wind farm in Cyprus are used to implement a simulation model in two different software programs featuring RES simulation capabilities. Power System Computer Aided Design (PSCAD) is used to investigate the electrical power output of a WT model, and KYAMOS software is used to investigate the modelling of the blades, the resulting primary torque and the resulting rotor rotational speed. The investigation result novelty is the establishment of the ability of the proposed, under development, software program to satisfy the three required characteristics. Therefore, Deviation from measured values of the turbine characteristics is observed for both programs, user interface and blade modelling, and the potential of integrating results of one software into the other is investigated.

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Algorithmic Stability of Heavy-Tailed Stochastic Gradient Descent on Least Squares 34

Algorithmic Stability of Heavy-Tailed Stochastic Gradient De...

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34th International Conference onAlgorithmic Learning Theory, ALT 2023

作者： Raj, Anant Barsbey, Melih Gürbüzbalaban, Mert Zhu, Lingjiong Şimşekli, Umut Coordinated Science Laboratory University of Illinois Urbana-Champaign Inria Ecole Normale Supérieure PSL Research University Paris France Department of Computer Engineering Bogaziçi University Istanbul Turkey Department of Management Science and Information Systems Rutgers University PiscatawayNJ United States Department of Mathematics Florida State University TallahasseeFL United States Inria CNRS Ecole Normale Supérieure PSL Research University Paris France

Recent studies have shown that heavy tails can emerge in stochastic optimization and that the heaviness of the tails have links to the generalization error. While these studies have shed light on interesting aspects of the generalization behavior in modern settings, they relied on strong topological and statistical regularity assumptions, which are hard to verify in practice. Furthermore, it has been empirically illustrated that the relation between heavy tails and generalization might not always be monotonic in practice, contrary to the conclusions of existing theory. In this study, we establish novel links between the tail behavior and generalization properties of stochastic gradient descent (SGD), through the lens of algorithmic stability. We consider a quadratic optimization problem and use a heavy-tailed stochastic differential equation (and its Euler discretization) as a proxy for modeling the heavy-tailed behavior emerging in SGD. We then prove uniform stability bounds, which reveal the following outcomes: (i) Without making any exotic assumptions, we show that SGD will not be stable if the stability is measured with the squared-loss x 7→ x2, whereas it in turn becomes stable if the stability is instead measured with a surrogate loss x 7→ |x|p with some p © 2023 A. Raj, M. Barsbey, M. Gürbüzbalaban, L. Zhu & U. Şimşekli.

关键词： Gradient methods

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Image Processing Implementation to Classify Coffee Fruit Ripeness Using K-Nearest Neighbor (KNN) Algorithm

Image Processing Implementation to Classify Coffee Fruit Rip...

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International Conference on Information management and Technology (ICIMTech)

作者： M. Farhan Hussaini Dermawan Deden Witarsyah Dedy Syamsuar Hanif Fakhrurroja Ahmad Luthfi Muhammad Izman Herdiansyah Faculty of Industrial Engineering Telkom University Bandung Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Informatics Faculty of Industrial Technology Islamic University of Indonesia Jogjakarta Indonesia Informatics Study Program Faculty of Science and Technology Universitas Bina Darma Palembang Indonesia

Coffee is one of the plantation crops that has long been a cultivated plant in Indonesia. The classification of coffee fruit maturity manually still has several weaknesses and requires a long process, has low accuracy and is inconsistent, this is because the determination is made subjectively by coffee farmers. As for the classification of coffee fruit maturity levels automatically, it can be faster with objective determination, therefore the use of image processing is relatively easier, faster, and based on a quantified descriptive assessment to determine coffee maturity. Image Processing is a method used to process or manipulate images in 2-dimensional form. In the classification process, there are many methods used to obtain classification of objects based on training data. One of the algorithms used for the classification process is K-Nearest Neighbor (KNN). KNN is a classification technique for objects based on training data that is the closest or has similar characteristics to the object. KNN includes supervised learning algorithms, where the results of the new query instance are classified based on the majority of the categories in K-Nearest Neighbors (K-NN). The finding indicated that class classification of ripe and unripe were 88,24 % and 100% respectively with 93,33% accuracy level.

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Linking Data Sovereignty and Data Economy: Arising Areas of Tension 17

Linking Data Sovereignty and Data Economy: Arising Areas of ...

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17th International Conference on Wirtschaftsinformatik, WI 2022

作者： Lauf, Florian Scheider, Simon Bartsch, Jan Herrmann, Philipp Radic, Marija Rebbert, Marcel Nemat, André T. Schlueter-Langdon, Christoph Konrad, Ralf Sunyaev, Ali Meister, Sven Fraunhofer Institute for Software and Systems Engineering ISST Healthcare Dortmund Germany TU Dortmund Chair of Industrial Information Management Dortmund Germany Department of Economics and Management Karlsruhe Germany Fraunhofer Center for International Management and Knowledge Economy IMW Corporate Development in International Competition Division Leipzig Germany Witten/Herdecke University Institute for Digital Transformation in Healthcare GmbH Witten Germany T-Systems International GmbH Digital Solutions - Data Intelligence Hub Frankfurt Germany Claremont Graduate University Drucker School of Management Claremont United States KASTEL Security Research Labs Karlsruhe Germany Witten/Herdecke University Faculty of Health School of Medicine Witten Germany

In the emerging information economy, data evolves as an essential asset and personal data in particular is used for data-driven business models. However, companies frequently leverage personal data without considering individuals’ data sovereignty. Therefore, we strive to strengthen individuals’ position in data ecosystems by combining concepts of data sovereignty and data economy. Our research design comprises an approach to design thinking iteratively generating, validating, and refining such concepts. As a result, we identified ten areas of tension that arise when linking data sovereignty and data economy. Subsequently, we propose initial solutions to resolve these tensions and thus contribute to knowledge about the development of fair data ecosystems benefiting both individuals’ sovereignty and companies’ access to data. © 2022 17th International Conference on Wirtschaftsinformatik, WI 2022. All rights reserved.

关键词： Ecosystems

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Wasserstein robust classification with fairness constraints

arXiv

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arXiv 2021年

作者： Wang, Yijie Nguyen, Viet Anh Hanasusanto, Grani A. Graduate Program in Operations Research and Industrial Engineering University of Texas at Austin Department of Management Science and Engineering Stanford University

We propose a distributionally robust classification model with a fairness constraint that encourages the classifier to be fair in view of the equality of opportunity criterion. We use a type-1 Wasserstein ambiguity set centered at the empirical distribution to model distributional uncertainty and derive a conservative reformulation for the worst-case equal opportunity unfairness measure. We establish that the model is equivalent to a mixed binary optimization problem, which can be solved by standard off-the-shelf solvers. To improve scalability, we further propose a convex, hinge-loss-based model for large problem instances whose reformulation does not incur any binary variables. Moreover, we also consider the distributionally robust learning problem with a generic ground transportation cost to hedge against the uncertainties in the label and sensitive attribute. Finally, we numerically demonstrate that our proposed approaches improve fairness with negligible loss of predictive accuracy. © 2021, CC BY.

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A Game-Theoretic Approach to Safe Crowd Evacuation in Emergencies

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Computers, Materials & Continua 2024年第4期79卷 1631-1657页

作者： Maria Gul Imran Ali Khan Gohar Zaman Atta Rahman Jamaluddin Mir Sardar Asad Ali Biabani May IssaAldossary Mustafa Youldash Ashraf Saadeldeen Maqsood Mahmud Asiya Abdus Salam Dania Alkhulaifi Abdullah AlTurkey31Department of Computer Science COMSATS University IslamabadAbbottabad CampusAbbottabad22010Pakistan Department of Computer Science Abbottabad University of Science and TechnologyAbbottabad22010Pakistan Department of Computer Science College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Science and Technology Unit Umm Al-Qura UniversityMakkah21955Saudi Arabia Deanship of Scientific Research Umm Al-Qura UniversityMakkah21955Saudi Arabia Department of Computer Information Systems College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Engineering College of Computer Science and Information TechnologyImam Abdulrahman Bin Faisal UniversityP.O.Box 1982Dammam31441Saudi Arabia Department of Computer Science Faculty of Computer Science and Information TechnologyOmdurman Islamic UniversityOmdurman14415Sudan Business Analytic Program Department of Management and MarketingCollege of Business AdministrationUniversity of BahrainSakhir32038Bahrain

Obstacle removal in crowd evacuation is critical to safety and the evacuation system efficiency. Recently, manyresearchers proposed game theoreticmodels to avoid and remove obstacles for crowd evacuation. Game theoreticalmodels aim to study and analyze the strategic behaviors of individuals within a crowd and their interactionsduring the evacuation. Game theoretical models have some limitations in the context of crowd evacuation. Thesemodels consider a group of individuals as homogeneous objects with the same goals, involve complex mathematicalformulation, and cannot model real-world scenarios such as panic, environmental information, crowds that movedynamically, etc. The proposed work presents a game theoretic model integrating an agent-based model to removethe obstacles from exits. The proposed model considered the parameters named: (1) obstacle size, length, andwidth, (2) removal time, (3) evacuation time, (4) crowd density, (5) obstacle identification, and (6) route *** proposed work conducts various experiments considering different conditions, such as obstacle types, obstacleremoval, and several obstacles. Evaluation results show the proposed model’s effectiveness compared with existingliterature in reducing the overall evacuation time, cell selection, and obstacle removal. The study is potentially usefulfor public safety situations such as emergency evacuations during disasters and calamities.

关键词： Safe crowd evacuation public safety emergency transition probability cooperation

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A preliminary study of the electrical and interlaminar shear properties of a porous CFRP composite laminate containing carbon nanofiber Z-threads 6

A preliminary study of the electrical and interlaminar shear...

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6th Annual Composites and Advanced Materials Expo, CAMX 2019

作者： Kirmse, Sebastian Ranabhat, Bikash Hsiao, Kuang-Ting University of South Alabama Mobile AL36688 United States Systems Engineering Program United States Department of Mechanical Engineering United States

In this study, a porous carbon fiber/epoxy nanocomposite (P-ZT-CFRP) containing a low concentration of z-threaded carbon nanofibers was manufactured. DC electrical conductivity measurements and interlaminar shear tests were performed on the porous nanocomposite. It was hypothesized that the CNFs interlock and connect multiple arrays of carbon fibers using a connecting agent (i.e., resin) at contact locations between the CNFs and carbon fibers, which provides additional strength to the otherwise weak porous composite laminate while making it extremely conductive. The through-thickness DC electrical conductivity was increased by 2840% (9.37 S/m) and 2971% (9.81 S/m) for the unidirectional porous nanocomposite laminate containing 0.85wt% and 2.0wt% of CNF z-threads, respectively, when compared to a traditional carbon fiber reinforced polymer (CFRP) (0.33 S/m). Void characterization showed that the void content in the matrix for the porous nanocomposites was at least 20%, which satisfies the definition of a porous nanocomposite [1]. The interlaminar shear strength of the 0.85wt% CNF z-threaded P-ZT-CFRP with a fiber volume fraction of around 80% was about 50% higher than for a P-CFRP without CNFs. Furthermore, the porous nanocomposite showed good potential in overcoming the effects of void on the mechanical properties of CFRPs. Using the rule of mixture, the P-ZT-CFRP shows a ~10.5% increase in longitudinal tensile strength and modulus when compared to a traditional CFRP. It was concluded that the material has high potential but will require additional research and research collaborations between industry partners to develop a possible next-generation multi-functional nanocomposite. © 2019 by Ever J. Barbero. Published by CAMX - The Composites and Advanced Materials Expo.

关键词： Carbon nanofibers

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IoT-based Efficient Waste management on University Campus: Enhancing Monitoring and Optimization through Machine Learning

IoT-based Efficient Waste Management on University Campus: E...

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3rd International Conference on Machine Learning and Data engineering, ICMLDE 2024

作者： Sumathi, M. Hanumanthappa, J. Santhosh Kumar, K.S. Shiva Prakash, S.P. Meghana, J. Department of Studies in Computer Science University of Mysore Manasagangothri Campus Mysuru Karnataka570006 India Department of Artificial Intelligence and Machine Learning Mysore University School of Engineering University of Mysore Manasagangothri Campus Mysuru Karnataka570006 India Department of Information Science and Engineering & Computer Science and Business Systems JSS Science and Technology University Mysuru Karnataka570006 India Department of Computer Science Mahajana Post Graduate Center Mysuru Karnataka570016 India

The introduction of an IoT-based smart waste management system is proposed to optimize various aspects of university campus waste recycle management processes, such as garbage collection scheduling, waste type identification, and collection routes. This system utilizes advanced data analytics algorithms, wireless communication protocols, and sensors to measure and document variations in trash levels, enabling real-time monitoring of waste accumulation. Through this IoT-based infrastructure, university administrators and waste management staff gain fine-grained visibility and control over garbage collection processes. The implications for sustainability advocacy and environmental stewardship in academic environments are significant. By fostering a culture of environmental awareness and accountability, universities can instill ideals of conservation and resourcefulness in their student populations and staff. The proposed IoT-enabled waste bin system exemplifies the synergy between technological innovation and environmental responsibility, serving as a model for institutions worldwide. Collaboration among universities can leverage IoT technology to drive positive change, mitigate environmental degradation, and promote sustainable practices beyond campus boundaries. Efficient waste management is crucial for nurturing sustainability on college and university campuses, as evidenced by the urgent need for the collection and recycling of compost, general waste, and recyclables. Our models consistently achieve high performance, with perfect scores (1.000) across all split ratios, indicating flawless classification of waste types. Although the Artificial Neural Network (ANN) demonstrates slightly lower accuracy, ranging from 0.973 to 0.997 depending on data splits, it still performs well. As a result, Naive Bayes produces less accurate results, typically surpassing 0.93 but falling short of Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)'s flawless

关键词： Electronic Waste

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Creating Simulated Equivalents to Project Long-Term Population Health Outcomes of Underserved Patients: An Application to Colorectal Cancer Screening

Creating Simulated Equivalents to Project Long-Term Populati...

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2021 Winter Simulation Conference, WSC 2021

作者： Koutouan, Priscille R. Mayorga, Maria E. O'Leary, Meghan C. Lich, Kristen Hassmiller North Carolina State University Department of Industrial and Systems Engineering RaleighNC27695-7906 United States University of North Carolina Department of Health Policy and Management Chapel HillNC27599 United States

ISBN: (纸本)9781665433112

Simulation models can be used to project the impact of interventions on long-term population health outcomes. To project the value of an intervention in a specific population, the model must be able to simulate individuals with similar characteristics and pathways as the population receiving the intervention. We aimed to estimate the long-term colorectal cancer (CRC) outcomes (cancers and deaths averted, life-years gained) associated with receipt of a first CRC screening through the Colorectal Cancer Control program (CRCCP) among low-income and underserved patients in the U.S. We recalibrate a simulation model previously calibrated based on a real-world mix of insurance and demographic factors for a particular state. We describe our strategy for developing simulated equivalents in terms of demographics, natural history, and CRC screening results for the CRCCP patients and matching these patients to their simulated equivalents. We then project lifetime CRC incidence and mortality with and without intervention. © 2021 IEEE.

关键词： Population statistics

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Principal Component Hierarchy for Sparse Quadratic programs 38

Principal Component Hierarchy for Sparse Quadratic Programs

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38th International Conference on Machine Learning, ICML 2021

作者： Vreugdenhil, Robbie Nguyen, Viet Anh Eftekhari, Armin Esfahani, Peyman Mohajerin Delft Center for Systems and Control Delft University of Technology Netherlands Department of Management Science and Engineering Stanford University United States VinAI Research Viet Nam Department of Mathematics and Mathematical Statistics Umea University Sweden

ISBN: (纸本)9781713845065

We propose a novel approximation hierarchy for cardinality-constrained, convex quadratic programs that exploits the rank-dominating eigenvectors of the quadratic matrix. Each level of approximation admits a min-max characterization whose objective function can be optimized over the binary variables analytically, while preserving convexity in the continuous variables. Exploiting this property, we propose two scalable optimization algorithms, coined as the "best response" and the "dual program", that can efficiently screen the potential indices of the nonzero elements of the original program. We show that the proposed methods are competitive with the existing screening methods in the current sparse regression literature, and it is particularly fast on instances with high number of measurements in experiments with both synthetic and real datasets. Copyright © 2021 by the author(s)

关键词： Quadratic programming

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