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Journal articles on the topic 'Topology with elements of learning'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

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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<p>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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Esparza-Gómez, Juan M., Héctor A. Guerrero-Osuna, Gerardo Ornelas-Vargas, and Luis F. Luque-Vega. "Deep Learning for greenhouse internal temperature forecast." International Journal of Combinatorial Optimization Problems and Informatics 14, no. 1 (2023): 86–94. https://doi.org/10.61467/2007.1558.2023.v14i1.345.

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The microclimate inside a greenhouse forecast has been a case of study in recent years; an adequate forecast of variables such as internal temperature helps farmers prevent losses in the harvest. In this investigation, the forecast of the greenhouse internal temperature is implemented through Recurrent Neural Networks (RNN) topology with Long-Short Term Memory (LSTM) algorithm. The analysis is performed with the many to one configuration for a sequence of three input elements and one output element for each of the year's four seasons. The metrics used for the analysis and validation of the dat

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Tejani, Ghanshyam G., Vimal J. Savsani, Vivek K. Patel, and Poonam V. Savsani. "Size, shape, and topology optimization of planar and space trusses using mutation-based improved metaheuristics." Journal of Computational Design and Engineering 5, no. 2 (2017): 198–214. http://dx.doi.org/10.1016/j.jcde.2017.10.001.

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Abstract In this study, simultaneous size, shape, and topology optimization of planar and space trusses are investigated. Moreover, the trusses are subjected to constraints for element stresses, nodal displacements, and kinematic stability conditions. Truss Topology Optimization (TTO) removes the superfluous elements and nodes from the ground structure. In this method, the difficulties arise due to unacceptable and singular topologies; therefore, the Grubler's criterion and the positive definiteness are used to handle such issue. Moreover, the TTO is challenging due to its search space, which

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Wang, Zhen, Yongjie Wang, Xinli Xiong, Qiankun Ren, and Jun Huang. "A Novel Framework for Enhancing Decision-Making in Autonomous Cyber Defense Through Graph Embedding." Entropy 27, no. 6 (2025): 622. https://doi.org/10.3390/e27060622.

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Faced with challenges posed by sophisticated cyber attacks and dynamic characteristics of cyberspace, the autonomous cyber defense (ACD) technology has shown its effectiveness. However, traditional decision-making methods for ACD are unable to effectively characterize the network topology and internode dependencies, which makes it difficult for defenders to identify key nodes and critical attack paths. Therefore, this paper proposes an enhanced decision-making method combining graph embedding with reinforcement learning algorithms. By constructing a game model for cyber confrontations, this pa

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RUDOLPH, GEORGE L., and TONY R. MARTINEZ. "LOCATION-INDEPENDENT TRANSFORMATIONS: A GENERAL STRATEGY FOR IMPLEMENTING NEURAL NETWORKS." International Journal on Artificial Intelligence Tools 03, no. 03 (1994): 417–27. http://dx.doi.org/10.1142/s0218213094000248.

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Most Artificial Neural Networks (ANNs) have a fixed topology during learning, and typically suffer from a number of short-comings as a result. Variations of ANNs that use dynamic topologies have shown ability to overcome many of these problems. This paper introduces Location-Independent Transformations (LITs) as a general strategy for implementing neural networks that use static and dynamic topologies. A LIT creates a set of location-independent nodes, where each node computes its part of the network output independent of other nodes, using local information. This type of transformation allows

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Башкиров, А. В., Н. В. Астахов, Н. В. Ципина, А. Б. Антиликаторов, and А. С. Демихова. "USING MACHINE LEARNING TO CALIBRATE ANALOG MICROCIRCUITS." ВЕСТНИК ВОРОНЕЖСКОГО ГОСУДАРСТВЕННОГО ТЕХНИЧЕСКОГО УНИВЕРСИТЕТА, no. 1 (April 19, 2021): 79–84. http://dx.doi.org/10.36622/vstu.2021.17.1.012.

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Предложен один из методов машинного обучения, который можно применить для разработки нейросети, предсказывающей габариты элементов аналоговых интегральных схем на примере двух усилителей, при этом учитываются их предполагаемые целевые характеристики. Эта научная работа показывает, что должным образом обученная нейросеть способна изучить шаблоны проектирования и генерировать схемы калибровки, которые являются адекватными и подходят под требования спецификаций, в том числе и тех, которые не содержались в обучающих данных. Представлены три варианта организации нейросети, по результатам анализа ко

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Lin, Deping, Tao Peng, Peiliang Zuo, and Wenbo Wang. "Deep-Reinforcement-Learning-Based Intelligent Routing Strategy for FANETs." Symmetry 14, no. 9 (2022): 1787. http://dx.doi.org/10.3390/sym14091787.

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Flying ad hoc networks (FANETs), which are composed of autonomous flying vehicles, constitute an important supplement to satellite networks and terrestrial networks, and they are indispensable for many scenarios including emergency communication. Unfortunately, the routing therein is largely affected by rapid topology changes, frequent disconnection of links, and a high vehicle mobility. In this paper, an intelligent routing strategy based on deep reinforcement learning (DRL) is proposed, which is decentralized and takes into account the status of symmetrical nodes in two hops. In order to per

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Li, Xiang, Fa Zhang, Ziyi Liu, et al. "Machine Learning-Driven Multimodal Feature Extraction and Optimization Strategies for High-Speed Railway Station Area." Land 14, no. 5 (2025): 1039. https://doi.org/10.3390/land14051039.

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The construction of high-speed railway (HSR) station areas serves as a crucial catalyst for urban spatial evolution. However, the absence of targeted urban management theories has led to widespread spatial resource waste and post-construction abandonment phenomena in these areas. Existing research predominantly focuses on development strategies for individual construction elements of HSR stations yet lacks comprehensive strategy formulation through coordinated multi-level elements from a sustainable perspective. This study establishes a national database comprising 1018 HSR station area sample

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Knyaz, Vladimir A., Vladimir V. Kniaz, Fabio Remondino, Sergey Y. Zheltov, and Armin Gruen. "3D Reconstruction of a Complex Grid Structure Combining UAS Images and Deep Learning." Remote Sensing 12, no. 19 (2020): 3128. http://dx.doi.org/10.3390/rs12193128.

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The latest advances in technical characteristics of unmanned aerial systems (UAS) and their onboard sensors opened the way for smart flying vehicles exploiting new application areas and allowing to perform missions seemed to be impossible before. One of these complicated tasks is the 3D reconstruction and monitoring of large-size, complex, grid-like structures as radio or television towers. Although image-based 3D survey contains a lot of visual and geometrical information useful for making preliminary conclusions on construction health, standard photogrammetric processing fails to perform den

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Lee, Sang-Hoon, Sangwon Seo, Soochang Park, and Tae-Sung Kim. "Fast Connectivity Construction via Deep Channel Learning Cognition in Beyond 5G D2D Networks." Electronics 11, no. 10 (2022): 1580. http://dx.doi.org/10.3390/electronics11101580.

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Along with the recent advance in wireless networking and data processing technologies, demands for low latency communication (LLC) are increasing in a wide variety of future-driven autonomous applications such as a smart factory, self-driving cars, and so on. The fifth generation of cellular mobile communications (5G) will cover this need as one of three key capacities in their usage scenarios: enhanced mobile broadband (eMBB), massive machine type communication (mMTC), and ultra-reliable low-latency communications (URLLC). The 5G systems are composed of mobile devices and various internet of

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Liu, Chao, Ting Zhao, and Nenggan Zheng. "DeepBranchTracer: A Generally-Applicable Approach to Curvilinear Structure Reconstruction Using Multi-Feature Learning." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 4 (2024): 3548–57. http://dx.doi.org/10.1609/aaai.v38i4.28143.

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Curvilinear structures, which include line-like continuous objects, are fundamental geometrical elements in image-based applications. Reconstructing these structures from images constitutes a pivotal research area in computer vision. However, the complex topology and ambiguous image evidence render this process a challenging task. In this paper, we introduce DeepBranchTracer, a novel method that learns both external image features and internal geometric characteristics to reconstruct curvilinear structures. Firstly, we formulate the curvilinear structures extraction as a geometric attribute es

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Poux, F., and J. J. Ponciano. "SELF-LEARNING ONTOLOGY FOR INSTANCE SEGMENTATION OF 3D INDOOR POINT CLOUD." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLIII-B2-2020 (August 12, 2020): 309–16. http://dx.doi.org/10.5194/isprs-archives-xliii-b2-2020-309-2020.

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Abstract. Automation in point cloud data processing is central for efficient knowledge discovery. In this paper, we propose an instance segmentation framework for indoor buildings datasets. The process is built on an unsupervised segmentation followed by an ontology-based classification reinforced by self-learning. We use both shape-based features that only leverages the raw X, Y, Z attributes as well as relationship and topology between voxel entities to obtain a 3D structural connectivity feature describing the point cloud. These are then used through a planar-based unsupervised segmentation

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Qiu, Cheng, Shanyi Du, and Jinglei Yang. "A deep learning approach for efficient topology optimization based on the element removal strategy." Materials & Design 212 (December 2021): 110179. http://dx.doi.org/10.1016/j.matdes.2021.110179.

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Shin, Dong-Jin, and Jeong-Joon Kim. "Deep Reinforcement Learning-Based Network Routing Technology for Data Recovery in Exa-Scale Cloud Distributed Clustering Systems." Applied Sciences 11, no. 18 (2021): 8727. http://dx.doi.org/10.3390/app11188727.

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Research has been conducted to efficiently transfer blocks and reduce network costs when decoding and recovering data from an erasure coding-based distributed file system. Technologies using software-defined network (SDN) controllers can collect and more efficiently manage network data. However, the bandwidth depends dynamically on the number of data transmitted on the network, and the data transfer time is inefficient owing to the longer latency of existing routing paths when nodes and switches fail. We propose deep Q-network erasure coding (DQN-EC) to solve routing problems by converging era

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Deng, Jun, Qingxia Li, and Wenhong Wei. "Improved Cascade Correlation Neural Network Model Based on Group Intelligence Optimization Algorithm." Axioms 12, no. 2 (2023): 164. http://dx.doi.org/10.3390/axioms12020164.

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The Cascade Correlation learning algorithm is a special supervised learning algorithm for artificial neural network architecture. The optimization algorithm in the traditional neural network has the disadvantages of a single optimization goal, slow convergence speed, and can easily fall into local area, which cannot fully meet the key elements in the cascade correlation learning algorithm. In comparison, the group intelligence optimization algorithm can take into account these key elements in the optimization process at the same time, and obtain better optimization results. In this paper, we p

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Savsani, Vimal J., Ghanshyam G. Tejani, Vivek K. Patel, and Poonam Savsani. "Modified meta-heuristics using random mutation for truss topology optimization with static and dynamic constraints." Journal of Computational Design and Engineering 4, no. 2 (2016): 106–30. http://dx.doi.org/10.1016/j.jcde.2016.10.002.

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AbstractIn this paper, simultaneous size and topology optimization of planar and space trusses subjected to static and dynamic constraints are investigated. All the benchmark trusses consider discrete cross-sectional areas to consider the practical aspect of manufacturing. Moreover, Trusses are considered with multiple loading conditions and subjected to constraints for natural frequencies, element stresses, nodal displacements, Euler buckling criteria, and kinematic stability conditions. Truss topology optimization (TTO) can be accomplished by the removal of superfluous elements and nodes fro

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Liu, Boya, Guoai Xu, Guosheng Xu, Chenyu Wang, and Peiliang Zuo. "Deep Reinforcement Learning-Based Intelligent Security Forwarding Strategy for VANET." Sensors 23, no. 3 (2023): 1204. http://dx.doi.org/10.3390/s23031204.

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The vehicular ad hoc network (VANET) constitutes a key technology for realizing intelligent transportation services. However, VANET is characterized by diverse message types, complex security attributes of communication nodes, and rapid network topology changes. In this case, how to ensure safe, efficient, convenient, and comfortable message services for users has become a challenge that should not be ignored. To improve the flexibility of routing matching multiple message types in VANET, this paper proposes a secure intelligent message forwarding strategy based on deep reinforcement learning

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Bunimovich, Leonid, D. J. Passey, Dallas Smith, and Benjamin Webb. "Spectral and Dynamic Consequences of Network Specialization." International Journal of Bifurcation and Chaos 30, no. 06 (2020): 2050091. http://dx.doi.org/10.1142/s0218127420500911.

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One of the hallmarks of real networks is the ability to perform increasingly complex tasks as their topology evolves. To explain this, it has been observed that as a network grows certain subsets of the network begin to specialize the function(s) they perform. A recent model of network growth based on this notion of specialization has been able to reproduce some of the most well-known topological features found in real-world networks including right-skewed degree distributions, the small world property, modular as well as hierarchical topology, etc. Here we describe how specialization under th

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Wu, Jia Ju, Ze You Pan, Yong Kang Xu, and Rong Yu. "The Study on Bayesian Network-Based IETM Information Retrieval Technology." Advanced Materials Research 846-847 (November 2013): 1180–84. http://dx.doi.org/10.4028/www.scientific.net/amr.846-847.1180.

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IETM has been an important role in equipment integrated support. IETM works mainly through the guidance of the equipment maintenance support information retrieval. So, IETM information retrieval technology is one of the key technologies in the application of integrated equipment support. This paper proposes a new method of IETM information query based on Bayesian network model. The model supports for the IETM information of structured query. IETM information query is based features, the use the IETM dataset words, the elements and structure of unit stats information on the topology of the mode

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Suzuki, Keijiro, Ryotaro Konoike, Shu Namiki, Hitoshi Kawashima, and Kazuhiro Ikeda. "O-band 32 × 32 silicon photonic switch with double Mach-Zehnder switch elements." Optics Express 33, no. 9 (2025): 20110. https://doi.org/10.1364/oe.558541.

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Silicon photonic switches are expected to offer energy efficient switching in future data center networks and artificial intelligence/machine learning clusters. In this paper, we report a strictly non-blocking silicon photonic switch with 32 inputs and 32 outputs based on a path-independent insertion-loss topology, which consists of double Mach-Zehnder (MZ) switch elements to improve the crosstalk performance. The switch chip, therefore, contains 2048 thermo-optic MZ switches. The switch chip was fabricated using a complementary metal-oxide-semiconductor pilot line with the 45-nm technology. T

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S, Krishna Narayanan, Dhanasekaran S, and Vasudevan V. "Depend Ability Analysis on CPS Using Machine Learning Techniques." Journal of Applied Engineering and Technological Science (JAETS) 5, no. 1 (2023): 339–48. http://dx.doi.org/10.37385/jaets.v5i1.3411.

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A Cyber-Physical System (CPS) is an entity that effortlessly monitors and controls physical operations by integrating computational and physical elements. Dependability on the CPS application program and also proposes a real-time analysis approach to CPS application dependability based upon Artificial Intelligence and Machine Learning (ML). For starters, pick complicated networks to determine tips within the system topology on the CPS application process. Unsupervised mastering category by a quick density clustering algorithm to classify the value of nodes could be successfully put on the cruc

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Guo, Sumin, Bo Wu, Jingyu Zhou, et al. "An Analog Circuit Fault Diagnosis Method Based on Circle Model and Extreme Learning Machine." Applied Sciences 10, no. 7 (2020): 2386. http://dx.doi.org/10.3390/app10072386.

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The fault diagnosis of analog circuits faces problems, such as inefficient feature extraction and fault identification. To solve the problems, this paper combines the circle model and the extreme learning machine (ELM) into a fault diagnosis method for the linear analog circuit. Firstly, a circle model for the voltage features of fault elements was established in the complex domain, according to the relationship between the circuit response, element position and circuit topology. To eliminate the impacts of tolerances and signal aliasing, the 3D feature was introduced to make the indistinguish

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Barkhatova, Daria A., Lyudmila B. Khegay, and Nikolai I. Pak. "Pedagogical design of “inverted” learning resources for home study." Perspectives of Science and Education 60, no. 6 (2022): 244–62. http://dx.doi.org/10.32744/pse.2022.6.14.

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Introduction. Self-education is becoming increasingly important in terms of the concept of lifelong learning. The changes in the ways of perceiving information, along with the new tendencies in the formation of “engaging” Internet content, cause a necessity to find new methods of presenting educational material aimed at efficient learning and meeting differing requirements of end users. The new demands of today’s young people on the self-education process actualise the need to describe the pedagogical design of “inverted” resource-transformers oriented towards productive self-learning in home

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Wang, Changhui. "Sample Density Clustering Method Considering Unbalanced Data Distribution." Mobile Information Systems 2022 (September 15, 2022): 1–8. http://dx.doi.org/10.1155/2022/7580468.

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The data distribution of the multidimensional array sensor is unbalanced in data sample collection. To improve the clustering ability of data samples, a data density clustering method of sparse scattered points and multisensor array sensor samples based on the analysis of unbalanced data distribution characteristics is proposed. The sparse scattered multisensor array network’s sample data collection structure is created using the Voronoi polygon topology. By analyzing the unbalanced parameters between data classes and reconstructing the characteristic space of data sample sequence, the time se

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Karathanasopoulos, Nikolaos, and Dimitrios C. Rodopoulos. "Enhanced Cellular Materials through Multiscale, Variable-Section Inner Designs: Mechanical Attributes and Neural Network Modeling." Materials 15, no. 10 (2022): 3581. http://dx.doi.org/10.3390/ma15103581.

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In the current work, the mechanical response of multiscale cellular materials with hollow variable-section inner elements is analyzed, combining experimental, numerical and machine learning techniques. At first, the effect of multiscale designs on the macroscale material attributes is quantified as a function of their inner structure. To that scope, analytical, closed-form expressions for the axial and bending inner element-scale stiffness are elaborated. The multiscale metamaterial performance is numerically probed for variable-section, multiscale honeycomb, square and re-entrant star-shaped

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Zhao, Bo, Dehui Zhang, Rongzhen Zhang, Zhu Li, Panpan Tang, and Haoming Wan. "Delineation and Analysis of Regional Geochemical Anomaly Using the Object-Oriented Paradigm and Deep Graph Learning—A Case Study in Southeastern Inner Mongolia, North China." Applied Sciences 12, no. 19 (2022): 10029. http://dx.doi.org/10.3390/app121910029.

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This research describes an advanced workflow of an object-based geochemical graph learning approach, termed OGE, which includes five key steps: (1) conduct the mean removal operation on the multi-elemental geochemical data and then normalize them; (2) data gridding and multiresolution segmentation; (3) calculate the Moran’s I value and construct the geochemical topology graph; (4) unsupervised deep graph learning; (5) the within-object statistical analysis. The final product of OGE is an object-based anomaly score map. The performance of OGE was demonstrated by a case study involving eighteen

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Putra, Endrawan Satria Mandala, and Hery Wahyudi. "PERENCANAAN ARSITEKTUR SISTEM INFORMASI SUMBER DAYA MANUSIA (SDM) DENGAN METODE FRAMEWORK TOGAF (STUDI KASUS : PUSAT PENGEMBANGAN SUMBER DAYA MANUSIA APARATUR KEMENTRIAN ENERGI DAN SUMBER DAYA MINERAL)." JURNAL COMPUTECH & BISNIS 14, no. 2 (2020): 82–89. https://doi.org/10.5281/zenodo.4320189.

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<em>Information Systems Architecture is an understanding of all the different elements that support enterprise development and how these elements relate. in the development of enterprise architecture modeling requires a framework in the hope of being able to manage complex systems and be able to align the Information Systems business to be developed. The Open Group Architecture Framework (TOGAF) is a framework that provides methods and tools for building, managing and implementing and maintaining enterprise architecture. The key elements of TOGAF are Architecture Development Method (ADM) which

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Zamrii, I. V., V. V. Sobchuk, and A. O. Barabash. "IDENTIFICATION OF INPUT ELEMENTS OF INFORMATION SPACE AND RESTORATION OF THEIR PARAMETERS IN THE SINGLE INFORMATION SPACE OF MANUFACTURING ENTERPRISE WITH CRITICAL INFRASTRUCTURE." Collection of scientific works of the Military Institute of Kyiv National Taras Shevchenko University, no. 75 (2022): 78–87. http://dx.doi.org/10.17721/2519-481x/2022/75-08.

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Modern production is impossible to imagine without integrated data management information systems that ensure the stability of technological, financial, logistical and other processes. The concept is based on the construction of a single information space based on advanced information technologies. One of the key aspects of building a single information space of the enterprise is the integration of automated systems of all divisions of the enterprise into a single information space. The implementation of such a concept is the key to improving the efficiency of production processes, reducing th

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Gong, Xiaomin, Shuangyin Ren, Chunjiang Wang, and Jingchao Wang. "Research on Computing Resource Measurement and Routing Methods in Software Defined Computing First Network." Sensors 24, no. 4 (2024): 1086. http://dx.doi.org/10.3390/s24041086.

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Computing resource measurement and computing routing are essential technologies in the computing first network (CFN), serving as its foundational elements. This paper introduces a Software Defined Computing First Network (SD-CFN) architecture. Building upon this framework, a Dynamic-Static Integrated Computing Resource Measurement Mechanism (DCRMM) is proposed, incorporating methods such as the entropy weight method and K-Means clustering. The DCRMM algorithm outperforms the Maximum-closest Static Algorithm (MSA) and Maximum Closest Dynamic Algorithm (MDA) in terms of node stability, node util

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Sahinovic, Armin, and Benjamin Geisler. "Quantifying transfer learning synergies in infinite-layer and perovskite nitrides, oxides, and fluorides." Journal of Physics: Condensed Matter 34, no. 21 (2022): 214003. http://dx.doi.org/10.1088/1361-648x/ac5995.

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Abstract We combine density functional theory simulations and active learning (AL) of element-embedding neural networks (NNs) to explore the sample efficiency for the prediction of vacancy layer formation energies and lattice parameters in ABX n infinite-layer (n = 2) versus perovskite (n = 3) nitrides, oxides, and fluorides in the spirit of transfer learning. Following a comprehensive data analysis from different thermodynamic, structural, and statistical perspectives, we show that NNs model these observables with high precision, using merely ∼ 30 % of the data for training and exclusively th

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Retiti Diop Emane, Christopher, Sangho Song, Hyeonbyeong Lee, et al. "Anomaly Detection Based on GCNs and DBSCAN in a Large-Scale Graph." Electronics 13, no. 13 (2024): 2625. http://dx.doi.org/10.3390/electronics13132625.

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Anomaly detection is critical across domains, from cybersecurity to fraud prevention. Graphs, adept at modeling intricate relationships, offer a flexible framework for capturing complex data structures. This paper proposes a novel anomaly detection approach, combining Graph Convolutional Networks (GCNs) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). GCNs, a specialized deep learning model for graph data, extracts meaningful node and edge representations by incorporating graph topology and attribute information. This facilitates learning expressive node embeddings cap

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Conversion, A. P. "Design optimization of auxetic metamaterial for airless tire using finite element method and machine learning." IOP Conference Series: Earth and Environmental Science 1500, no. 1 (2025): 012042. https://doi.org/10.1088/1755-1315/1500/1/012042.

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Abstract Auxetic metamaterials which are characterized by a negative Poisson’s ratio, exhibit unique mechanical properties, expanding or contracting transversely under uniaxial compression or tension. These materials have drawn significant interest in biomedicine, aerospace, impact protection, and in automotives for airless tires which contribute to sustainability goals by improving material efficiency and reducing waste. Auxetic materials are designed using conventional methods such as designs of experiment where microstructures with geometric parameters are linked to specific mechanical prop

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Pan, Chen, Jiaming Guo, Haibo Li, Jiawei Wu, Nengjie Qiu, and Shengzhen Wu. "Study on the Influence Mechanism of Machine-Learning-Based Built Environment on Urban Vitality in Macau Peninsula." Buildings 15, no. 9 (2025): 1557. https://doi.org/10.3390/buildings15091557.

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Clarifying the mechanisms by which the micro-scale built environment influences urban vitality is an important scientific challenge, to guide precise urban planning in the context of urban renewal. In this study, we quantify the intensity of human activities through Baidu heat maps, analyze social interaction patterns using social media check-in data, and integrate built environment elements such as road network topology, 3D building morphology, and the spatial distribution of points of interest (POIs). A machine learning technique combining a real-encoded Accelerated Genetic Algorithm-Project

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Fitria, Tira Nur. "Augmented Reality (AR) and Virtual Reality (VR) Technology in Education: Media of Teaching and Learning: A Review." International Journal of Computer and Information System (IJCIS) 4, no. 1 (2023): 14–25. https://doi.org/10.29040/ijcis.v4i1.102.

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Augmented Reality (AR) and Virtual Reality (VR) are the latest technologies that provide a new color to the field of education. This study reviews the use of AR and VR in education, especially in the teaching and learning process. This study is library research. The analysis shows that AR and VR are the solutions for teachers and students as a medium in the teaching and learning process. AR embellishes existing reality with image elements, sound effects, or text. While, VR creates a new simulation environment that presents a specific topic to students in an engaging, interactive, and experient

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Choi, Jae, Yuzhou Chen, Huikyo Lee, Hyun Kim, and Yulia R. Gel. "SNN-PDE: Learning Dynamic PDEs from Data with Simplicial Neural Networks." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (2024): 11561–69. http://dx.doi.org/10.1609/aaai.v38i10.29038.

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Dynamics of many complex systems, from weather and climate to spread of infectious diseases, can be described by partial differential equations (PDEs). Such PDEs involve unknown function(s), partial derivatives, and typically multiple independent variables. The traditional numerical methods for solving PDEs assume that the data are observed on a regular grid. However, in many applications, for example, weather and air pollution monitoring delivered by the arbitrary located weather stations of the National Weather Services, data records are irregularly spaced. Furthermore, in problems involving

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Cao, Hailin, Wang Zhu, Wenjuan Feng, and Jin Fan. "Robust Beamforming Based on Graph Attention Networks for IRS-Assisted Satellite IoT Communications." Entropy 24, no. 3 (2022): 326. http://dx.doi.org/10.3390/e24030326.

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Satellite communication is expected to play a vital role in realizing Internet of Remote Things (IoRT) applications. This article considers an intelligent reflecting surface (IRS)-assisted downlink low Earth orbit (LEO) satellite communication network, where IRS provides additional reflective links to enhance the intended signal power. We aim to maximize the sum-rate of all the terrestrial users by jointly optimizing the satellite’s precoding matrix and IRS’s phase shifts. However, it is difficult to directly acquire the instantaneous channel state information (CSI) and optimal phase shifts of

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Blanco, L., E. Zeydan, S. Barrachina-Munoz, F. Rezazadeh, L. Vettori, and J. Mangues-Bafalluy. "A Novel Approach for Scalable and Sustainable 6G Networks." Ieee Open Journal Of The Communications Society 5 (January 1, 2024): 1673–92. https://doi.org/10.1109/OJCOMS.2024.3372426.

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Hierarchical, distributed, scalable and Artificial Intelligence (AI)-based management of a massive number of network slices in different domains with the goal of zero-touch management is a major challenge for 6G networks. In this paper, we first propose a new vision for distributed network management and orchestration based on existing standardization architectures. This vision aims to embed AI/Machine Learning (ML) into the AI/ML architectures of Standardization Development Organizations (SDOs) such as the 3rd Generation Partnership Project (3GPP), the European Telecommunications Standards In

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Jiang, Ying, Minghui Nian, and Qinghui Zhang. "A Stable Generalized Finite Element Method Coupled with Deep Neural Network for Interface Problems with Discontinuities." Axioms 11, no. 8 (2022): 384. http://dx.doi.org/10.3390/axioms11080384.

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The stable generalized finite element method (SGFEM) is an improved version of generalized or extended FEM (GFEM/XFEM), which (i) uses simple and unfitted meshes, (ii) reaches optimal convergence orders, and (iii) is stable and robust in the sense that conditioning is of the same order as that of FEM and does not get bad as interfaces approach boundaries of elements. This paper designs the SGFEM for the discontinuous interface problem (DIP) by coupling a deep neural network (DNN). The main idea is to construct a function using the DNN, which captures the discontinuous interface condition, and

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Scionti, Alberto, Somnath Mazumdar, and Antoni Portero. "Towards a Scalable Software Defined Network-on-Chip for Next Generation Cloud." Sensors 18, no. 7 (2018): 2330. http://dx.doi.org/10.3390/s18072330.

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The rapid evolution of Cloud-based services and the growing interest in deep learning (DL)-based applications is putting increasing pressure on hyperscalers and general purpose hardware designers to provide more efficient and scalable systems. Cloud-based infrastructures must consist of more energy efficient components. The evolution must take place from the core of the infrastructure (i.e., data centers (DCs)) to the edges (Edge computing) to adequately support new/future applications. Adaptability/elasticity is one of the features required to increase the performance-to-power ratios. Hardwar

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Grey, Simon, and Neil A. Gordon. "Motivating Students to Learn How to Write Code Using a Gamified Programming Tutor." Education Sciences 13, no. 3 (2023): 230. http://dx.doi.org/10.3390/educsci13030230.

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Engagement and retention are widely acknowledged problems in computer science and more general higher education. The need to develop programming skills is increasingly ubiquitous, but especially so in computer science where it is one of the core competencies. Learning to write code is a particularly challenging skill to master, which can make retention and success even more difficult. We attempt to address student engagement within an introductory programming module by attempting to motivate students using a gamified interactive programming tutor application that provides immediate feedback on

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Bale, Arron, Robert Rambo, and Christopher Prior. "The SKMT Algorithm: A method for assessing and comparing underlying protein entanglement." PLOS Computational Biology 19, no. 11 (2023): e1011248. http://dx.doi.org/10.1371/journal.pcbi.1011248.

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We present fast and simple-to-implement measures of the entanglement of protein tertiary structures which are appropriate for highly flexible structure comparison. These are performed using the SKMT algorithm, a novel method of smoothing the Cα backbone to achieve a minimal complexity curve representation of the manner in which the protein’s secondary structure elements fold to form its tertiary structure. Its subsequent complexity is characterised using measures based on the writhe and crossing number quantities heavily utilised in DNA topology studies, and which have shown promising results

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