Готові списки джерел за темами / Topology with elements of learning

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Добірка наукової літератури з теми "Topology with elements of learning"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 липня 2025

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Статті в журналах з теми "Topology with elements of learning"

Kim, Chyon Hae, Tetsuya Ogata, and Shigeki Sugano. "Reinforcement Signal Propagation Algorithm for Logic Circuit." Journal of Robotics and Mechatronics 20, no. 5 (2008): 757–74. http://dx.doi.org/10.20965/jrm.2008.p0757.

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This paper proposes a group of network elements, SONE, that self-organizes network topology, aiming at online and real-time learning and adaptation in robots. SONE, consisting of node elements and link elements, develops network topology by repeating generation and elimination of themselves based on reinforcement signals that are propagated and stored between the elements. This technique proved successful in simulations in which a mobile robot avoided obstacles, and it convinced us of its feasibility for online learning.

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Noilublao, Norapat, and Sujin Bureerat. "Simultaneous Topology, Shape, and Sizing Optimisation of Plane Trusses with Adaptive Ground Finite Elements Using MOEAs." Mathematical Problems in Engineering 2013 (2013): 1–9. http://dx.doi.org/10.1155/2013/838102.

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This paper proposes a novel integrated design strategy to accomplish simultaneous topology shape and sizing optimisation of a two-dimensional (2D) truss. An optimisation problem is posed to find a structural topology, shape, and element sizes of the truss such that two objective functions, mass and compliance, are minimised. Design constraints include stress, buckling, and compliance. The procedure for an adaptive ground elements approach is proposed and its encoding/decoding process is detailed. Two sets of design variables defining truss layout, shape, and element sizes at the same time are

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Noilublao, Chaid, and Sujin Bureerat. "Topology and Sizing Optimization of Trusses with Adaptive Ground Finite Elements Using Multiobjective PBIL." Advanced Materials Research 308-310 (August 2011): 1116–21. http://dx.doi.org/10.4028/www.scientific.net/amr.308-310.1116.

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This paper presents an integrated design technique to carry out simultaneous topology and sizing optimization of a two-dimensional truss structure. An optimization problem is set to find a structural layout and elements’ cross-sectional areas of a 2D truss such that objective functions including mass, compliance, and buckling factor are optimized. Design constraints consist of stress, buckling, and compliance. The concept of an adaptive ground elements approach and the encoding/decoding process are detailed. The multiobjective version of population-based incremental learning (PBIL) is employed

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Saghayi, Marzie, Jonathan Greenberg, Christopher O’Grady, et al. "Brain network topology predicts participant adherence to mental training programs." Network Neuroscience 4, no. 3 (2020): 528–55. http://dx.doi.org/10.1162/netn_a_00136.

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Adherence determines the success and benefits of mental training (e.g., meditation) programs. It is unclear why some participants engage more actively in programs for mental training than others. Understanding neurobiological factors that predict adherence is necessary for understanding elements of learning and to inform better designs for new learning regimens. Clustering patterns in brain networks have been suggested to predict learning performance, but it is unclear whether these patterns contribute to motivational aspects of learning such as adherence. This study tests whether configuratio

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Xian Guo, Xian Guo, Jianing Yang Xian Guo, Zhanhui Gang Jianing Yang, and An Yang Zhanhui Gang. "Research on Network Security Situation Awareness and Dynamic Game Based on Deep Q Learning Network." 網際網路技術學刊 24, no. 2 (2023): 537–51. http://dx.doi.org/10.53106/160792642023032402030.

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In today’s increasingly complex network environment, increasingly severe network attacks, and real-time dynamic changes in offensive and defensive scenarios, network security technology has also evolved from passive security to active security technology, and expanded from analyzing unilateral security elements to comprehensively analyzing overall network security. Aiming at the problem of inaccurate assessment results due to the lack of comprehensive analysis of threat information, protection information and environmental information in existing assessment methods, this paper propose

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Nicola, Marcel, and Claudiu-Ionel Nicola. "Comparative Performance Analysis of the DC-AC Converter Control System Based on Linear Robust or Nonlinear PCH Controllers and Reinforcement Learning Agent." Sensors 22, no. 23 (2022): 9535. http://dx.doi.org/10.3390/s22239535.

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Starting from the general topology and the main elements that connect a microgrid represented by a DC power source to the main grid, this article presents the performance of the control system of a DC-AC converter. The main elements of this topology are the voltage source inverter represented by a DC-AC converter and the network filters. The active Insulated Gate Bipolar Transistor (IGBT) or Metal–Oxide–Semiconductor Field-Effect Transistor (MOSFET) elements of the DC-AC converter are controlled by robust linear or nonlinear Port Controlled Hamiltonian (PCH) controllers. The outputs of these c

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Fischer, M. M. "Computational Neural Networks: A New Paradigm for Spatial Analysis." Environment and Planning A: Economy and Space 30, no. 10 (1998): 1873–91. http://dx.doi.org/10.1068/a301873.

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In this paper a systematic introduction to computational neural network models is given in order to help spatial analysts learn about this exciting new field. The power of computational neural networks viz-à-viz conventional modelling is illustrated for an application field with noisy data of limited record length: spatial interaction modelling of telecommunication data in Austria. The computational appeal of neural networks for solving some fundamental spatial analysis problems is summarized and a definition of computational neural network models in mathematical terms is given. Three definiti

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Sleesongsom, S., and S. Bureerat. "Morphing Wing Structural Optimization Using Opposite-Based Population-Based Incremental Learning and Multigrid Ground Elements." Mathematical Problems in Engineering 2015 (2015): 1–16. http://dx.doi.org/10.1155/2015/730626.

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This paper has twin aims. Firstly, a multigrid design approach for optimization of an unconventional morphing wing is proposed. The structural design problem is assigned to optimize wing mass, lift effectiveness, and buckling factor subject to structural safety requirements. Design variables consist of partial topology, nodal positions, and component sizes of a wing internal structure. Such a design process can be accomplished by using multiple resolutions of ground elements, which is called a multigrid approach. Secondly, an opposite-based multiobjective population-based incremental learning

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Medvedeva, Irina, and Olga Ryabova. "THE USE OF PROJECT TECHNOLOGY TO INTRODUCE SCHOOLCHILDREN TO ELEMENTS OF THE GENERAL TOPOLOGY." SOCIETY. INTEGRATION. EDUCATION. Proceedings of the International Scientific Conference 2 (May 28, 2021): 376–86. http://dx.doi.org/10.17770/sie2021vol2.6211.

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Preparing a student with project skills is one of the urgent tasks facing education. At present, the general education school of Russia is moving to new educational standards, which provide for the mandatory formation of students' skills in using project activities. The article contains an analysis of the experience of involving students of different age groups in project activities during their acquaintance with the basic topological concepts within the framework of the author's elective discipline "Get to know topology".At the preparatory stage of teaching, the content of the modern section

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Ullah, Abid, Karim Asami, Lukas Holtz, et al. "A Machine Learning Approach for Mechanical Component Design Based on Topology Optimization Considering the Restrictions of Additive Manufacturing." Journal of Manufacturing and Materials Processing 8, no. 5 (2024): 220. http://dx.doi.org/10.3390/jmmp8050220.

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Additive manufacturing (AM) and topology optimization (TO) emerge as vital processes in modern industries, with broad adoption driven by reduced expenses and the desire for lightweight and complex designs. However, iterative topology optimization can be inefficient and time-consuming for individual products with a large set of parameters. To address this shortcoming, machine learning (ML), primarily neural networks, is considered a viable tool to enhance topology optimization and streamline AM processes. In this work, a machine learning (ML) model that generates a parameterized optimized topol

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Дисертації з теми "Topology with elements of learning"

SUAREZ, MIGUEL ANGEL AMPUERO. "TOPOLOGY OPTIMIZATION FOR EIGENVALUE PROBLEMS USING POLYGONAL FINITE ELEMENTS." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2016. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=28017@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR PROGRAMA DE EXCELENCIA ACADEMICA Neste trabalho, são apresentadas algumas aplicações da otimização topológica para problemas de autovalor onde o principal objetivo é maximizar um determinado autovalor, como por exemplo uma frequência natural de vibração ou uma carga crítica linearizada, usando elementos finitos poligonais em domínios bidimensionais arbitrários. A otimização topológica tem sido comumente utilizada para minimizar a flexibilidade de estruturas sujeitas a restri

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Ahmed, Shoaib. "Topology optimization of load-carryingstructures using three different typesof finite elements." Thesis, KTH, Optimeringslära och systemteori, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-124147.

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This thesis deals with topology optimization of load-carrying structures, in particular compliance minimization subject to a constraint on the total amount of material to be used. The main purpose of the work was to compare the following three types of finite elements for the above topology optimization problems: Four node square elements with bilinear shape functions, nine node square elements with quadratic shape functions, and six node hexagonal elements with Wachspress shape functions. The SIMP approach (Solid Isotropic Material with Penalization) was used to model the topology optimizatio

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Tomov, Petar Georgiev. "Interplay of dynamics and network topology in systems of excitable elements." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät, 2016. http://dx.doi.org/10.18452/17464.

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Wir untersuchen globale dynamische Phänomene, die sich von dem Zusammenspiel zwischen Netzwerktopologie und Dynamik der einzelnen Elementen ergeben. Im ersten Teil untersuchen wir relativ kleine strukturierte Netzwerke mit überschaubarer Komplexität. Als geeigneter theoretischer Rahmen für erregbare Systeme verwenden wir das Kuramoto und Shinomoto Modell der sinusförmig-gekoppelten "aktiven Rotatoren" und studieren das Kollektivverhalten des Systems in Bezug auf Synchronisation. Wir besprechen die Einschränkungen, die durch die Netzwerktopologie auf dem Fluss im Phasenraum des Systems geste

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Welti-Santos, Larissa 1978. "Topology of learning and correction in dynamic balance." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/26742.

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Thesis (S.M.)--Massachusetts Institute of Technology, School of Architecture and Planning, Program in Media Arts and Sciences, 2004. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Includes bibliographical references (p. 95-98). "The discrepancy between [our personal experiences] and our idealizations of knowledge leads us into counterproductive strategies for learning and thinking" (Papert 1993). This discrepancy has driven most research towards understanding how people fail to accompli

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Bhela, Siddharth. "Load Learning and Topology Optimization for Power Networks." Diss., Virginia Tech, 2019. http://hdl.handle.net/10919/90407.

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With the advent of distributed energy resources (DERs), electric vehicles, and demand-response programs, grid operators are in dire need of new monitoring and design tools that help improve efficiency, reliability, and stability of modern power networks. To this end, the work in this thesis explores a generalized modeling and analysis framework for two pertinent tasks: i) learning loads via grid probing, and; ii) optimizing power grid topologies for stability. Distribution grids currently lack comprehensive real-time metering. Nevertheless, grid operators require precise knowledge of loads and

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Rawat, Sharad. "DEEP LEARNING BASED FRAMEWORK FOR STRUCTURAL TOPOLOGY DESIGN." The Ohio State University, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1559560543458263.

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Amigo, Ricardo Cesare Roman. "Design of adsorption systems by topology optimisation." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/3/3152/tde-05112018-140813/.

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Adsorption is a retention mechanism of fluid molecules on solid surfaces and presents a wide range of applications, such as refrigeration, separation processes and fuel storage. This work describes the modelling of gas adsorption on porous media and presents an optimisation approach for the design of adsorption systems based on topology optimisation. A comprehensive formulation for the adsorption and desorption phenomena is presented, detailing the derivation of their weak forms and assembling the matrices for the implementation of the Finite Element Method (FEM). The sensitivities of objectiv

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Sun, Shiyao M. Eng Massachusetts Institute of Technology. "Nonlinear analysis of topology-optimized scissor-like elements during deployment and structural performance analysis." Thesis, Massachusetts Institute of Technology, 2020. https://hdl.handle.net/1721.1/129015.

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Thesis: M. Eng., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, September, 2020 Cataloged from student-submitted PDF of thesis. Includes bibliographical references (pages 55-58). Deployable structures, known for their flexibility and adaptability, have gained popularity in applications such as emergency shelters, aerospace structures, and sports facility roofs. One of many commonly used deployable mechanisms is Scissor-Like Elements, which is generally made from two straight rods connected by a scissor hinge. To improve the quality of the fle

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Перепеліцин, Сергій Олександрович, та Sergiy Perepelitsyn. "Технологія налаштовування радіомережі в умовах завад інтеграцією маршрутизації та самонавчання". Thesis, Національний авіаційний університет, 2021. https://er.nau.edu.ua/handle/NAU/49767.

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Дисертаційна робота присвячене розв'язання науково-технічної задачі зі створення інформаційної технології моделювання ефективного контролю за топологією однорангової мобільної радіомережі, що само налагоджується, тактичного рівня й управління зміною показників її функціонування в умовах впливу радіоперешкод та радіоелектронної протидії (РЕБ). У дисертаційній роботі вперше запропоновано нова топологія, що відрізняється від відомих тим, що включає елементи навчання поведінки мережі в умовах перешкод. Введені нові процеси інтелектуальної системи керування вузлом мобільної радіомережі: пошукова

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Cordero, Maisonet Sixto. "Learning topology : engaging the development of spatial cognition though adpositional play." Thesis, Massachusetts Institute of Technology, 2016. http://hdl.handle.net/1721.1/103425.

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Thesis: M. Arch., Massachusetts Institute of Technology, Department of Architecture, 2016. This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections. Cataloged from student-submitted PDF version of thesis. Includes bibliographical references (page 81). This thesis establishes a qualitative analysis of current playground design and challenges the minimal role that playgrounds play in education, spatial cognition, and the development of a child's understanding of space. McDonalds' playgrounds were

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Книги з теми "Topology with elements of learning"

Baum, John D. Elements of point set topology. Dover Publications, 1991.

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Novikov, S. P., and A. T. Fomenko. Basic Elements of Differential Geometry and Topology. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-015-7895-0.

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Novikov, S. P. Basic elements of differential geometry and topology. Kluwer Academic, 1990.

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Novikov, Sergeĭ Petrovich. Basic elements of differential geometry and topology. Kluwer Academic Publishers, 1990.

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Newman, M. H. A. Elements of the topology of plane sets of points. Greenwood Press, 1985.

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Newman, M. H. A. Elements of the topology of plane sets of points. 2nd ed. Dover Publications, 1992.

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Nicolas Bourbaki. Elements of mathematics. Springer-Verlag, 1990.

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1930-, Cannon Harold C., and NetLibrary Inc, eds. The elements of learning. Yale University Press, 1999.

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1930-, Cannon Harold C., ed. The elements of learning. Yale University Press, 1999.

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Peterson, R. Elements of instructing. Edutran Communications, 1986.

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Частини книг з теми "Topology with elements of learning"

Nanni, Luca. "Computational Inference of DNA Folding Principles: From Data Management to Machine Learning." In Special Topics in Information Technology. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-85918-3_7.

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AbstractDNA is the molecular basis of life and would total about three meters if linearly untangled. To fit in the cell nucleus at the micrometer scale, DNA has, therefore, to fold itself into several layers of hierarchical structures, which are thought to be associated with functional compartmentalization of genomic features like genes and their regulatory elements. For this reason, understanding the mechanisms of genome folding is a major biological research problem. Studying chromatin conformation requires high computational resources and complex data analyses pipelines. In this chapter, we

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Basu, Saugata, Richard Pollack, and Marie-Francoise Roy. "Elements of Topology." In Algorithms in Real Algebraic Geometry. Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-662-05355-3_7.

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Ziemer, William P. "Elements of Topology." In Modern Real Analysis. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-64629-9_3.

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Denkowski, Zdzisław, Stanisław Migórski, and Nikolas S. Papageorgiou. "Elements of Topology." In An Introduction to Nonlinear Analysis: Theory. Springer US, 2003. http://dx.doi.org/10.1007/978-1-4419-9158-4_1.

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Gracia-Bondía, José M., Joseph C. Várilly, and Héctor Figueroa. "Noncommutative Topology: Spaces." In Elements of Noncommutative Geometry. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0005-5_1.

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Novikov, S. P., and A. T. Fomenko. "Basic Elements of Topology." In Mathematics and Its Applications. Springer Netherlands, 1990. http://dx.doi.org/10.1007/978-94-015-7895-0_3.

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Patrascu, Andrei-Tudor. "Elements of General Topology." In The Universal Coefficient Theorem and Quantum Field Theory. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-46143-4_2.

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Bourbaki, Nicolas. "Contents of the Elements of Mathematics Series." In General Topology. Springer Berlin Heidelberg, 1995. http://dx.doi.org/10.1007/978-3-642-61701-0_1.

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Gracia-Bondía, José M., Joseph C. Várilly, and Héctor Figueroa. "Noncommutative Topology: Vector Bundles." In Elements of Noncommutative Geometry. Birkhäuser Boston, 2001. http://dx.doi.org/10.1007/978-1-4612-0005-5_2.

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Munkres, James R., Steven G. Krantz, and Harold R. Parks. "Homological Algebra." In Elements of Algebraic Topology, 2nd ed. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003621478-7.

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Тези доповідей конференцій з теми "Topology with elements of learning"

Zhang, Yueguo, Lili Dong, and Xinghao Jiang. "Legal judgment prediction based on computer topology learning and fact elements." In CONF-CDS 2021: The 2nd International Conference on Computing and Data Science. ACM, 2021. http://dx.doi.org/10.1145/3448734.3450488.

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Sriram, Anirudh Roshan, DeLean Tolbert, Monica Cardella, and Karthik Ramani. "Bridging the Gaps: Augmenting Design Learning Through Computer-Aided Exploration." In ASME 2015 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/detc2015-46978.

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In helping students learn engineering design, it is very important that they explore complex scenarios that are realistic, and fall outside the domain of standard and over-simplified textbook problems that typically have an answer. A majority of the current educational methods and computer-based tools do not bridge this gap and lack affordances for design exploration. Although computational methods such as Finite Element Analysis have this potential, they are hard to use requiring the users to spend a significant effort. Also, several instructors have identified significant knowledge gaps in c

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Rastegarzadeh, Sina, Jun Wang, and Jida Huang. "Multi-Scale Topology Optimization With Neural Network-Assisted Optimizer." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-89538.

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Abstract High-resolution structural designs attracts researchers to multi-scale topology optimizations (TO) paradigms. With the advances of machine learning (ML) methods, the integration of ML with TO has been attempted in many works. However, most works employ ML in a data-driven paradigm, which requires abundant training data. The generalization ability of such a data-driven paradigm is also ambiguous. This research aims to utilize the machine learning techniques as an optimizer for multi-scale structural design problems to address the connectivity issues of adjacent microstructures, a commo

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Lourenço, Vítor N., Gabriela G. Silva, and Leandro A. F. Fernandes. "Hierarchy-of-Visual-Words: a Learning-based Approach for Trademark Image Retrieval." In XXXII Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/sibgrapi.est.2019.8332.

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In this paper, we present the Hierarchy-of-Visual-Words (HoVW), a novel trademark image retrieval (TIR) method that decomposes images into simpler geometric shapes and defines a descriptor for binary trademark image representation by encoding the hierarchical arrangement of component shapes. The proposed hierarchical organization of visual data stores each component shape as a visual word. It is capable of representing the geometry of individual elements and the topology of the trademark image, making the descriptor robust against linear as well as to some level of nonlinear transformation. Ex

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Saito, Akihiro, Nobukazu Takai, and Satoshi Konno. "Determination of circuit topology and element values from desired characteristics by machine learning." In 2020 27th IEEE International Conference on Electronics, Circuits and Systems (ICECS). IEEE, 2020. http://dx.doi.org/10.1109/icecs49266.2020.9294885.

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Kuang, Quanyu, Zhang Rui, Song Zhang, and Gao Yixuan. "Combine HD Map Information to Enhance Road Topology Reasoning by Offline Supervised Training and on-Line Ensemble Learning." In WCX SAE World Congress Experience. SAE International, 2025. https://doi.org/10.4271/2025-01-8021.

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<div class="section abstract"><div class="htmlview paragraph">Topology reasoning plays a crucial role in understanding complex driving scenarios and facilitating downstream planning, yet the process of perception is inevitably affected by weather, traffic obstacles and worn lane markings on road surface. Combine pre-produced High-definition maps (HDMaps), and other type of map information to the perception network can effectively enhance perception robustness, but this on-line fused information often requires a real-time connection to website servers. We are exploring the possibili

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VISWANATH, ASHA, MOHAMAD MODREK, KAMRAN A. KHAN, and RASHID K. ABU AL-RUB. "DEEP LEARNING FOR TOPOLOGY OPTIMIZATION OF TRIPLY PERIODIC MINIMAL SURFACE BASED GYROID-LIKE STRUCTURES." In Thirty-sixth Technical Conference. Destech Publications, Inc., 2021. http://dx.doi.org/10.12783/asc36/35824.

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Triply periodic minimal surfaces (TPMS) are non-intersecting complex geometrical surfaces that can be used in unit cell design of cellular structures. TPMS possess attractive properties like large surface area to volume ratio and mathematically controlled geometry which find them applications in catalytic converters, cocontinuous composites, thermal and permeability management, to name a few. The advent of additive manufacturing eased the manufacture of these structures which were previously challenging with traditional methods of manufacturing. Design of TPMS unit-cell based materials involve

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Sharpe, Conner, and Carolyn Conner Seepersad. "Topology Design With Conditional Generative Adversarial Networks." In ASME 2019 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/detc2019-97833.

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Abstract Deep convolutional neural networks have gained significant traction as effective approaches for developing detailed but compact representations of complex structured data. Generative networks in particular have become popular for their ability to mimic data distributions and allow further exploration of them. This attribute can be utilized in engineering design domains, in which the data structures of finite element meshes for analyzing potential designs are well suited to the deep convolutional network approaches that are being developed at a rapid pace in the field of image processi

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Brown, Ashland O. "Undergraduate Finite Element Instruction Using Commercial Finite Element Software Tutorials and the Kolb Learning Cycle." In ASME 2004 International Mechanical Engineering Congress and Exposition. ASMEDC, 2004. http://dx.doi.org/10.1115/imece2004-60756.

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As background, the Kolb learning cycle describes an entire cycle around which a learning experience progresses [1]. The goal, therefore, is to structure learning activities that will proceed completely around this cycle, providing the maximum opportunity for full student comprehension of the course material. This model has been used previously to evaluate and enhance teaching in engineering [2, 3, and 4]. Most college education is geared toward abstract conceptualiztion, but complete learning is enhanced by the use of all four learning stages Abstract Hypothesis and Conceptualization, Active E

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Liu, Kai, Duane Detwiler, and Andres Tovar. "Machine Learning and Metamodel-Based Design Optimization of Nonlinear Multimaterial Structures." In ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-60471.

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This study presents an efficient multimaterial design optimization algorithm that is suitable for nonlinear structures. The proposed algorithm consists of three steps: conceptual design generation, design characterization by machine learning, and metamodel-based multi-objective optimization. The conceptual design can be generated from extracting finite element analysis information or by using structure optimization. The conceptual design is then characterized by using machine learning techniques to dramatically reduce the dimension of the design space. Finally, metamodels are derived using Eff

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Звіти організацій з теми "Topology with elements of learning"

Goudarzi, Alireza. On the Effect of Topology on Learning and Generalization in Random Automata Networks. Portland State University Library, 2000. http://dx.doi.org/10.15760/etd.193.

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Rockmore, Daniel. Dynamic Information Networks: Geometry, Topology and Statistical Learning for the Articulation of Structure. Defense Technical Information Center, 2015. http://dx.doi.org/10.21236/ada624183.

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Short, Jim Short, and Stephanie Hirsh Hirsh. The Elements: Transforming Teaching through Curriculum-Based Professional Learning. Carnegie Corporation of New York, 2020. http://dx.doi.org/10.15868/socialsector.37787.

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Sadeh, Jana. Keeping students engaged with elements of flipped, collaborative and adaptive learning techniques. The Economics Network, 2023. http://dx.doi.org/10.53593/n3640a.

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Darling-Hammond, Linda, Marjorie E. Wechsler, Stephanie Levin, Melanie Leung-Gagne, and Steve Tozer. Developing effective principals: What kind of learning matters? Learning Policy Institute, 2022. http://dx.doi.org/10.54300/641.201.

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This report reviews the research literature since 2000 to understand the elements of high-quality programs and learning experiences that have been associated with positive outcomes ranging from principals’ sense of preparedness, efficacy, and reported practices to staff perceptions of school climate and retention to student achievement. It also examines the extent to which principals have opportunities to participate in learning experiences with those elements and the policies that drive both the development of high-quality programs and access to them.

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London, Jonathan. Vietnam’s Education System: How Coherent Is It for Learning? Research on Improving Systems of Education (RISE), 2023. http://dx.doi.org/10.35489/bsg-rise-wp_2023/131.

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The disappointing performance of education systems in developing countries in improving learning outcomes has spurred research aimed at establishing what features of education systems determine their effectiveness or failure in improving learning outcomes. There has been special interest in the challenge of making education systems more coherent for learning, i.e., developing systems in which accountability relations among stakeholders across key elements of education policy design support and sustain strong learning outcomes. In the emergent literature on the political economy of education, a

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Ferdaus, Md Meftahul, Mahdi Abdelguerfi, Elias Ioup, et al. KANICE : Kolmogorov-Arnold networks with interactive convolutional elements. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49791.

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We introduce KANICE, a novel neural architecture that combines Convolutional Neural Networks (CNNs) with Kolmogorov-Arnold Network (KAN) principles. KANICE integrates Interactive Convolutional Blocks (ICBs) and KAN linear layers into a CNN framework. This leverages KANs’ universal approximation capabilities and ICBs’ adaptive feature learning. KANICE captures complex, non-linear data relationships while enabling dynamic, context-dependent feature extraction based on the Kolmogorov-Arnold representation theorem. We evaluated KANICE on four datasets: MNIST, Fashion-MNIST, EMNIST, and SVHN, compa

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Thorpe, Jodie, Alisha Ault, Iana Barenboim, Luize Guimarães, Evert-jan Quak, and Katia Taela. Learning from Entrepreneurship Programming for Women’s Economic Empowerment. Institute of Development Studies, 2023. http://dx.doi.org/10.19088/muva.2023.001.

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MUVA is a social incubator dedicated to developing innovative approaches to the economic empowerment of women in Mozambique. This paper documents experiences from two MUVA projects supporting women’s economic empowerment through entrepreneurship, and draws out broader insights and principles of relevance to other similar programmes. Barriers to women’s economic empowerment and strategies to overcome these barriers are both individual and systemic, visible and invisible. MUVA’s approach to supporting women’s economic empowerment through entrepreneurship involves tailoring three core elements to

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Kropf, Heather. Residence Life as Learning Organizations: An Inquiry Into Organizational Elements that Support Integration of the Residential Curriculum. Portland State University Library, 2020. http://dx.doi.org/10.15760/etd.7336.

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Rosser, Andrew, Phil King, and Danang Widoyoko. The Political Economy of the Learning Crisis in Indonesia. Research on Improving Systems of Education (RISE), 2022. http://dx.doi.org/10.35489/bsg-rise-2022/pe01.

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Indonesia has done much to improve access to education in recent decades but it has had little success in improving learning outcomes. This paper examines the political origins of this problem. It argues that Indonesia’s learning crisis has reflected the political dominance during the New Order and post-New Order periods of predatory political, bureaucratic and corporate elites who have sought to use the country’s education system to accumulate resources, distribute patronage, mobilize political support, and exercise political control rather than produce skilled workers and critical and inquir

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