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Journal articles on the topic 'Load graph'

Author: Grafiati

Published: 30 May 2022

Last updated: 31 July 2025

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1

Rutten, Daan, and Debankur Mukherjee. "Load Balancing Under Strict Compatibility Constraints." ACM SIGMETRICS Performance Evaluation Review 49, no. 1 (2022): 51–52. http://dx.doi.org/10.1145/3543516.3456275.

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Consider a system with N identical single-server queues and M(N) task types, where each server is able to process only a small subset of possible task types. Arriving tasks select d≥2 random compatible servers, and join the shortest queue among them. The compatibility constraints are captured by a fixed bipartite graph GN between the servers and the task types. When GN is complete bipartite, the meanfield approximation is accurate. However, such dense compatibility graphs are infeasible for large-scale implementation. We characterize a class of sparse compatibility graphs for which the meanfie

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Sidorov, V. V., A. Z. Ter-Martirosyan, and A. S. Almakaeva. "Determination of application limits of graph-analytical method of a single pile settlement calculation." Zhilishchnoe Stroitel stvo, no. 11 (December 11, 2024): 47–54. https://doi.org/10.31659/0044-4472-2024-11-47-54.

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The limits of application of the graph-analytical method for calculating the settlement of a single pile in a single-layer soil massif depending on the length and diameter of the pile are investigated. The graph-analytical method makes it possible to take into account non-linear work of the soil on the whole surface of the pile, exclusion from further work of the soil area along the lateral surface of the pile, where the ultimate strength of the soil was reached, mechanism of load distribution on the pile and friction reduction at the contact “pile–soil”. A comparative analysis of the results

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Cui, Huanqing, Yifan Wu, and Silin Lv. "Property Graph Partition Algorithm Based on Improved Barnacle Mating Optimization." Journal of Physics: Conference Series 2832, no. 1 (2024): 012005. http://dx.doi.org/10.1088/1742-6596/2832/1/012005.

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Abstract Distributed graph processing systems have been used more frequently in various fields, and graph partitioning is the basis of these systems. Graph partitioning algorithms generally aim to minimize the communication cost while the number of vertices or edges reach the load balance. But the vertices and/or edges of property graphs require some storage volume, so the traditional graph partition algorithms will lead to an unbalanced storage volume load. This paper proposes an edge-cut graph partitioning algorithm to generate partitions with equal size and storage volume as well as low cut

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Ai, Xin, Hao Yuan, Zeyu Ling, et al. "NeutronTP: Load-Balanced Distributed Full-Graph GNN Training with Tensor Parallelism." Proceedings of the VLDB Endowment 18, no. 2 (2024): 173–86. https://doi.org/10.14778/3705829.3705837.

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Graph neural networks (GNNs) have emerged as a promising direction. Training large-scale graphs that relies on distributed computing power poses new challenges. Existing distributed GNN systems leverage data parallelism by partitioning the input graph and distributing it to multiple workers. However, due to the irregular nature of the graph structure, existing distributed approaches suffer from unbalanced workloads and high overhead in managing cross-worker vertex dependencies. In this paper, we leverage tensor parallelism for distributed GNN training. GNN tensor parallelism eliminates cross-w

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Seon, Yu-Rim, Chul Min Lee, and Duk Ho Chung. "Study on the Middle School Students’ Cognitive Load When Interpreting the Graphs on Sea Surface Temperature around the Korean Peninsula." Korean Association For Learner-Centered Curriculum And Instruction 24, no. 19 (2024): 351–65. http://dx.doi.org/10.22251/jlcci.2024.24.19.351.

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Objectives The purpose of this study is to identify the cognitive load that middle school students perceive in interpreting of sea surface temperature around the Korean Peninsula according to the type of graph, and to help middle school teachers develop teaching and learning materials by providing information related to cognitive load. Methods For This, we collected and analyzed data on the fixation duration and pupil diameter of middle school students(N=32) while interpreting of sea surface temperature around the Korean Peninsula using the eye tracker(Tobii Pro Glass 2 model). And we conducte

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Bok, Kyoungsoo, Junwon Kim, and Jaesoo Yoo. "Dynamic Partitioning Supporting Load Balancing for Distributed RDF Graph Stores." Symmetry 11, no. 7 (2019): 926. http://dx.doi.org/10.3390/sym11070926.

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Various resource description framework (RDF) partitioning methods have been studied for the efficient distributed processing of a large RDF graph. The RDF graph has symmetrical characteristics because subject and object can be used interchangeably if predicate is changed. This paper proposes a dynamic partitioning method of RDF graphs to support load balancing in distributed environments where data insertion and change continue to occur. The proposed method generates clusters and subclusters by considering the usage frequency of the RDF graph that are used by queries as the criteria to perform

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Sidorov, Vitaly, Armen Ter-Martirosyan, and Anastasiya Almakaeva. "Graph-analytical calculating pile’s settlement verification." E3S Web of Conferences 533 (2024): 02044. http://dx.doi.org/10.1051/e3sconf/202453302044.

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The paper presents the solution of the problem of interaction of a single reinforced concrete compressible pile with an elastoplastic single-layer soil mass. The settlement is calculated on the basis of the analytical equation in the elastic formulation. A graph-analytical method is proposed to account for the nonlinear behavior of soils, which limits the bearing capacity of the pile on the lateral surface to the strength of the adjacent soil and under the pile toe to the ultimate load determined by the Brinch Hansen formula. The novelty of this method consists in taking into account the mecha

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Paulin, S. Shiny, and T. Bharathi. "Adjacency Sequence and Adjacency Spectrum of Power Fuzzy Graphs." Indian Journal Of Science And Technology 17, no. 47 (2024): 5016–24. https://doi.org/10.17485/ijst/v17i47.3605.

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Objectives: To find the adjacency sequence, spectrum of the power fuzzy graphs and discuss its properties. Methods: The spectrum and energy of the power fuzzy graphs are derived using the adjacency matrices. Energy of the power fuzzy graph is computed by adding the absolute eigenvalues of the adjacency matrix. Findings: The condition for a power fuzzy graph, 𝐺𝑓 𝑘 to be vertex regular in terms of adjacency sequence has been verified. The energy sequence for the power fuzzy graph with increasing 𝑘 has been established. Novelty: The concept of minimizing the interval of the edge membership values

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S, Shiny Paulin, and Bharathi T. "Adjacency Sequence and Adjacency Spectrum of Power Fuzzy Graphs." Indian Journal of Science and Technology 17, no. 47 (2024): 5016–24. https://doi.org/10.17485/IJST/v17i47.3605.

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Abstract <strong>Objectives:</strong> To find the adjacency sequence, spectrum of the power fuzzy graphs and discuss its properties.<strong> Methods:</strong> The spectrum and energy of the power fuzzy graphs are derived using the adjacency matrices. Energy of the power fuzzy graph is computed by adding the absolute eigenvalues of the adjacency matrix. <strong>Findings:</strong> The condition for a power fuzzy graph, 𝐺𝑓 𝑘 to be vertex regular in terms of adjacency sequence has been verified. The energy sequence for the power fuzzy graph with increasing 𝑘 has been estab

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Ding, Zezhong, Yongan Xiang, Shangyou Wang, Xike Xie, and S. Kevin Zhou. "Play like a Vertex: A Stackelberg Game Approach for Streaming Graph Partitioning." Proceedings of the ACM on Management of Data 2, no. 3 (2024): 1–27. http://dx.doi.org/10.1145/3654965.

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In the realm of distributed systems tasked with managing and processing large-scale graph-structured data, optimizing graph partitioning stands as a pivotal challenge. The primary goal is to minimize communication overhead and runtime cost. However, alongside the computational complexity associated with optimal graph partitioning, a critical factor to consider is memory overhead. Real-world graphs often reach colossal sizes, making it impractical and economically unviable to load the entire graph into memory for partitioning. This is also a fundamental premise in distributed graph processing,

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Yang, Carl, Aydın Buluç, and John D. Owens. "GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU." ACM Transactions on Mathematical Software 48, no. 1 (2022): 1–51. http://dx.doi.org/10.1145/3466795.

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High-performance implementations of graph algorithms are challenging to implement on new parallel hardware such as GPUs because of three challenges: (1) the difficulty of coming up with graph building blocks, (2) load imbalance on parallel hardware, and (3) graph problems having low arithmetic intensity. To address some of these challenges, GraphBLAS is an innovative, on-going effort by the graph analytics community to propose building blocks based on sparse linear algebra, which allow graph algorithms to be expressed in a performant, succinct, composable, and portable manner. In this paper, w

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Draief, M., A. Ganesh, and L. Massoulié. "Exponential Random Graphs as Models of Overlay Networks." Journal of Applied Probability 46, no. 01 (2009): 199–220. http://dx.doi.org/10.1017/s0021900200005313.

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In this paper we give an analytic solution for graphs with n nodes and E = cn log n edges for which the probability of obtaining a given graph G is µn (G) = exp (- β ∑i=1 n d i 2), where d i is the degree of node i. We describe how this model appears in the context of load balancing in communication networks, namely peer-to-peer overlays. We then analyse the degree distribution of such graphs and show that the degrees are concentrated around their mean value. Finally, we derive asymptotic results for the number of edges crossing a graph cut and use these results (i) to compute the graph expans

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Yu, Feng, Hongshi Tan, Xinyu Chen, Yao Chen, Bingsheng He, and Weng-Fai Wong. "Clementi: Efficient Load Balancing and Communication Overlap for Multi-FPGA Graph Processing." Proceedings of the ACM on Management of Data 3, no. 3 (2025): 1–27. https://doi.org/10.1145/3725275.

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Efficient graph processing is critical in various modern applications, such as social network analysis, recommendation systems, and large-scale data mining. Traditional single-FPGA systems struggle to handle the increasing size and complexity of real-world graphs due to limitations in memory and computational resources. Existing multi-FPGA solutions face significant challenges, including high communication overhead caused by irregular data transfer patterns and workload imbalances stemming from skewed graph distributions. These inefficiencies hinder scalability and performance, highlighting a

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Zhao, Junhao, Xiaodong Shen, Youbo Liu, Junyong Liu, and Xisheng Tang. "Enhancing Aggregate Load Forecasting Accuracy with Adversarial Graph Convolutional Imputation Network and Learnable Adjacency Matrix." Energies 17, no. 18 (2024): 4583. http://dx.doi.org/10.3390/en17184583.

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Accurate load forecasting, especially in the short term, is crucial for the safe and stable operation of power systems and their market participants. However, as modern power systems become increasingly complex, the challenges of short-term load forecasting are also intensifying. To address this challenge, data-driven deep learning techniques and load aggregation technologies have gradually been introduced into the field of load forecasting. However, data quality issues persist due to various factors such as sensor failures, unstable communication, and susceptibility to network attacks, leadin

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Moharir, Sharayu, Sujay Sanghavi, and Sanjay Shakkottai. "Online Load Balancing Under Graph Constraints." IEEE/ACM Transactions on Networking 24, no. 3 (2016): 1690–703. http://dx.doi.org/10.1109/tnet.2015.2442597.

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Moharir, Sharayu, Sujay Sanghavi, and Sanjay Shakkottai. "Online load balancing under graph constraints." ACM SIGMETRICS Performance Evaluation Review 41, no. 1 (2013): 363–64. http://dx.doi.org/10.1145/2494232.2465751.

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Chen, Wenzhi, Hongjian Sun, Minglei You, Jing Jiang, and Marco Rivera. "A Knowledge Graph-Based Framework for Smart Home Device Action Recommendation and Demand Response." Energies 18, no. 4 (2025): 833. https://doi.org/10.3390/en18040833.

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Within smart homes, consumers could generate a vast amount of data that, if analyzed effectively, can improve the convenience of consumers and reduce energy consumption. In this paper, we propose to organize household appliance data into a knowledge graph by using the consumers’ usage habits, the periods of usage, and the location information for graph modeling. A framework, ‘DARK’ (Device Action Recommendation with Knowledge graphs), is proposed that includes three parts for enabling demand response. Firstly, a household device action recommendation algorithm is proposed that improves the kno

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Draief, M., A. Ganesh, and L. Massoulié. "Exponential Random Graphs as Models of Overlay Networks." Journal of Applied Probability 46, no. 1 (2009): 199–220. http://dx.doi.org/10.1239/jap/1238592125.

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In this paper we give an analytic solution for graphs withnnodes andE=cnlognedges for which the probability of obtaining a given graphGisµn(G) = exp (-β∑i=1ndi2), wherediis the degree of nodei. We describe how this model appears in the context of load balancing in communication networks, namely peer-to-peer overlays. We then analyse the degree distribution of such graphs and show that the degrees are concentrated around their mean value. Finally, we derive asymptotic results for the number of edges crossing a graph cut and use these results (i) to compute the graph expansion and conductance, a

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COSNARD, M., and M. LOI. "AUTOMATIC TASK GRAPH GENERATION TECHNIQUES." Parallel Processing Letters 05, no. 04 (1995): 527–38. http://dx.doi.org/10.1142/s0129626495000473.

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We present a model of parallel computation, the parameterized task graph, which is a compact, problem size independent, representation of some frequently used directed acyclic task graphs. Techniques automating the construction of such a representation, starting from an annotated sequential program are proposed. We show that many important properties of the task graph such as the computational load of the nodes and the communication volume of the edges can be automatically deduced in a problem size independent way.

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Sharma, Bhuvan, Van C. Willis, Claudia S. Huettner, et al. "Predictive article recommendation using natural language processing and machine learning to support evidence updates in domain-specific knowledge graphs." JAMIA Open 3, no. 3 (2020): 332–37. http://dx.doi.org/10.1093/jamiaopen/ooaa028.

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Abstract Objectives Describe an augmented intelligence approach to facilitate the update of evidence for associations in knowledge graphs. Methods New publications are filtered through multiple machine learning study classifiers, and filtered publications are combined with articles already included as evidence in the knowledge graph. The corpus is then subjected to named entity recognition, semantic dictionary mapping, term vector space modeling, pairwise similarity, and focal entity match to identify highly related publications. Subject matter experts review recommended articles to assess inc

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Zhao, Mengjie, Cees Taal, Stephan Baggerohr, and Olga Fink. "Virtual Sensor for Real-Time Bearing Load Prediction Using Heterogeneous Temporal Graph Neural Networks." PHM Society European Conference 8, no. 1 (2024): 8. http://dx.doi.org/10.36001/phme.2024.v8i1.3998.

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Accurate bearing load monitoring is essential for their Prognostics and Health Management (PHM), enabling damage assessment, wear prediction, and proactive maintenance. While bearing sensors are typically placed on the bearing housing, direct load monitoring requires sensors inside the bearing itself. Recently introduced sensor rollers enable direct bearing load monitoring but are constrained by their battery life. Data-driven virtual sensors can learn from sensor roller data collected during a battery's lifetime to map operating conditions to bearing loads.Although spatially distributed beari

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Samia, Latreche. "MODELING AND OPTIMIZATION OF PIEZOELECTRIC ENERGY HARVESTING." Advances in Engineering: an International Journal (ADEIJ), Vol.2, No.4 2, no. 4 (2019): 01–12. https://doi.org/10.5281/zenodo.3547374.

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In this paper, the modeling, optimization and simulation results of the piezoelectric energy harvesting using bond graph approach are presented. Firstly, a lightweight equivalent model derived from the bond graph is proposed. It’s a comprehensive model, which is suitable for piezoelectric seismic energy harvester investigation and power optimization. The optimal charge impedance for both the resistive load and complex load are given and analysed. Finally a bond graph approach is proposed to allow optimization of the extracted energy while keeping simplicity and standalone capability. The

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Haja Syed Hussain, Mohd Ridzuan Mohd Jamir, Mohd Shukry Abdul Majid, Ahmad Syayuthi Abdul Rahman, Mohd Azaman Md Deros, and Mohd Hafis Sulaiman. "Tribological Behaviour of Furcraea Foetida Fiber-Reinforced Epoxy Composites under Varying Applied Loads." Journal of Advanced Research in Applied Sciences and Engineering Technology 33, no. 3 (2023): 98–111. http://dx.doi.org/10.37934/araset.33.3.98111.

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This study investigates the tribological behaviour of Furcraea Foetida fibre-reinforced epoxy composites under varying applied loads. The aim is to understand the influence of applied load on the frictional behaviour and wear characteristics of the composites. The study employs a pin-on-disk test to examine the frictional force, coefficient of friction (COF), and specific wear rate (SWR) of identical samples subjected to four different loads (80 N, 100 N, 120 N, 140 N). Scanning electron microscopy (SEM) is used to analyse the surface morphology under different loads. The frictional force vers

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Alistarh, Dan, Giorgi Nadiradze, and Amirmojtaba Sabour. "Dynamic Averaging Load Balancing on Cycles." Algorithmica 84, no. 4 (2021): 1007–29. http://dx.doi.org/10.1007/s00453-021-00905-9.

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AbstractWe consider the following dynamic load-balancing process: given an underlying graph G with n nodes, in each step $$t\ge 0$$ t ≥ 0 , a random edge is chosen, one unit of load is created, and placed at one of the endpoints. In the same step, assuming that loads are arbitrarily divisible, the two nodes balance their loads by averaging them. We are interested in the expected gap between the minimum and maximum loads at nodes as the process progresses, and its dependence on n and on the graph structure. Peres et al. (Random Struct Algorithms 47(4):760–775, 2015) studied the variant of this

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Alobaidi, Isam A., Hiba G. Fareed, Jennifer L. Leopold, and Andrea E. Smit. "Maximizing edge connectivity in graph partitioning using hotspots." International Journal of Data and Network Science 9, no. 3 (2025): 385–94. https://doi.org/10.5267/j.ijdns.2025.4.002.

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Graphs have long been used to model relationships between entities. For some applications, a single graph is sufficient; for other problems, a collection of graphs may be more appropriate to represent the underlying data. Many contemporary problem domains, for which graphs are an ideal data model, contain an enormous amount of data (e.g., social networks). Hence, researchers frequently employ parallelized or distributed processing. The graph data must first be partitioned and assigned to the multiple processors in a way that the workload is balanced and inter-processor communication is minimiz

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Zainullina, R. "Automatic Graph Generation for E-learning Systems." Bulletin of Science and Practice 7, no. 6 (2021): 12–16. http://dx.doi.org/10.33619/2414-2948/67/01.

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The subject of the research is one of the ways of updating modern training systems for solving problems of graph theory, namely, automatic generation of graphs. This approach will reduce the load on the training system database and generate tasks for the user in real-time without updating the bank of tasks. In the course of the work, the advantages and disadvantages of this approach were identified. The most suitable method for the implementation of the research was chosen to represent graphs in electronic computers. The requirements for generated graphs and possible ways of implementing these

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Chen, Zhigang, Haotian Peng, and Yongxin Su. "Nonintrusive load disaggregation by fusion of graph signal processing and CNN." Journal of Physics: Conference Series 2853, no. 1 (2024): 012061. http://dx.doi.org/10.1088/1742-6596/2853/1/012061.

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Abstract Nonintrusive load disaggregation techniques play a pivotal role in power system planning and decision making for demand response. The sparsity of low frequency total power data features, coupled with the model’s challenge in effectively extracting and utilizing spatial and temporal correlations in diverse load data, presents obstacles to achieving high precision disaggregation in nonintrusive load disaggregation. To address these challenges, this paper proposes a disaggregation framework that integrates graph signal processing (GSP) with a convolutional neural network (CNN). In this f

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Feng, Wenshan, Zhuo Long, Gongping Wu, Liwei Xie, Jinping Xie, and Fengqin Huang. "Grid Load Forecasting Based On Spatio-temporal Graphical Information." Journal of Physics: Conference Series 3012, no. 1 (2025): 012023. https://doi.org/10.1088/1742-6596/3012/1/012023.

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Abstract The forecast of power grid load can predict future power demand. This helps power companies plan their power generation effectively. Additionally, it aids in allocating power resources wisely. Thus power companies ensure that the supply capacity meets the demand. This prevents power shortages or overloads caused by supply and demand imbalances. This paper proposes a spatio-temporal graph information-based load forecasting method that extracts and analyzes the spatio-temporal correlation properties of the graph information by converting time-series data into Markov field graph informat

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Jeurissen, R., and W. Layton. "Load balancing by graph coloring, an algorithm." Computers & Mathematics with Applications 27, no. 3 (1994): 27–32. http://dx.doi.org/10.1016/0898-1221(94)90043-4.

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Lü, Yashuai, Hui Guo, Libo Huang, et al. "GraphPEG." ACM Transactions on Architecture and Code Optimization 18, no. 3 (2021): 1–24. http://dx.doi.org/10.1145/3450440.

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Due to massive thread-level parallelism, GPUs have become an attractive platform for accelerating large-scale data parallel computations, such as graph processing. However, achieving high performance for graph processing with GPUs is non-trivial. Processing graphs on GPUs introduces several problems, such as load imbalance, low utilization of hardware unit, and memory divergence. Although previous work has proposed several software strategies to optimize graph processing on GPUs, there are several issues beyond the capability of software techniques to address. In this article, we present Graph

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Choi, Dojin, Jinsu Han, Jongtae Lim, Jinsuk Han, Kyoungsoo Bok, and Jaesoo Yoo. "Dynamic Graph Partitioning Scheme for Supporting Load Balancing in Distributed Graph Environments." IEEE Access 9 (2021): 65254–65. http://dx.doi.org/10.1109/access.2021.3075457.

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Musaev, Timur, Marat Khabibullin, Ramil Kamaliev, Oleg Fedorov, Ilgiz Valeev, and Oleg Vladimirov. "Influence of smart metering systems on increasing the accuracy of calculation electrical power losses in electrical networks." E3S Web of Conferences 220 (2020): 01026. http://dx.doi.org/10.1051/e3sconf/202022001026.

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The article discusses the possibility of using data from smart electricity meters (SEM) to increase the accuracy of calculation losses in 0.4 kV low-voltage networks. An increase in accuracy can be achieved using actual data about the load graph of electricity consumers (in this case, 6(10)-0.4 kV transformer substations are meant). To date, the operating load factor is taken equal to 0.5, which does not always correspond to the actual data. Using SEM, actual load graphs can be obtained, which enable more accurate determination of the operating load factor. Consequently, the accuracy of calcul

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Huang, Weidong, Peter Eades, and Seok-Hee Hong. "Measuring Effectiveness of Graph Visualizations: A Cognitive Load Perspective." Information Visualization 8, no. 3 (2009): 139–52. http://dx.doi.org/10.1057/ivs.2009.10.

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Graph visualizations are typically evaluated by comparing their differences in effectiveness, measured by task performance such as response time and accuracy. Such performance-based measures have proved to be useful in their own right. There are some situations, however, where the performance measures alone may not be sensitive enough to detect differences. This limitation can be seen from the fact that the graph viewer may achieve the same level of performance by devoting different amounts of cognitive effort. In addition, it is not often that individual performance measures are consistently

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Shao, Jun Peng, Jian Ying Li, Zhong Wen Wang, and Gui Hua Han. "Research on Electro-Hydraulic Load Simulator Based on Building Model of Flow Press Servo Valve." Advanced Materials Research 129-131 (August 2010): 213–17. http://dx.doi.org/10.4028/www.scientific.net/amr.129-131.213.

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The model of flow press servo valve is built in this paper, during building the model, the author emphatically analyses the flow equation and force (torque) balance equation of every part of the valve, at the same time, all levels sub-models are organic combined according to power flow direction, signal flow direction of elements and causality, then we get the bond graph model of the flow press servo from this way. Adapting flow press servo valve and flow servo valve to concurrently control load system has its great advantage in restraining the superfluous force of the electro-hydraulic load s

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Wang, Zuoxin, and Xiaohu Zhao. "AttG-BDGNets: Attention-Guided Bidirectional Dynamic Graph IndRNN for Non-Intrusive Load Monitoring." Information 14, no. 7 (2023): 383. http://dx.doi.org/10.3390/info14070383.

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Most current non-intrusive load monitoring methods focus on traditional load characteristic analysis and algorithm optimization, lack knowledge of users’ electricity consumption behavior habits, and have poor accuracy. We propose a novel attention-guided bidirectional dynamic graph IndRNN approach. The method first extends sequence or multidimensional data to a topological graph structure. It effectively utilizes the global context by following an adaptive graph topology derived from each set of data content. Then, the bidirectional Graph IndRNN network (Graph IndRNN) encodes the aggregated si

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Huang, Chaokai, Ning Du, Jiahan He, Na Li, Yifan Feng, and Weihong Cai. "Multidimensional Feature-Based Graph Attention Networks and Dynamic Learning for Electricity Load Forecasting." Energies 16, no. 18 (2023): 6443. http://dx.doi.org/10.3390/en16186443.

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Electricity load forecasting is of great significance for the overall operation of the power system and the orderly use of electricity at a later stage. However, traditional load forecasting does not consider the change in load quantity at each time point, while the information on the time difference of the load data can reflect the dynamic evolution information of the load data, which is a very important factor for load forecasting. In addition, the research topics in recent years mainly focus on the learning of the complex relationships of load sequences in time latitude by graph neural netw

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Weng, Wentao, Xingyu Zhou, and R. Srikant. "Optimal Load Balancing with Locality Constraints." ACM SIGMETRICS Performance Evaluation Review 49, no. 1 (2022): 49–50. http://dx.doi.org/10.1145/3543516.3456279.

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Applications in cloud platforms motivate the study of efficient load balancing under job-server constraints and server heterogeneity. In this paper, we study load balancing on a bipartite graph where left nodes correspond to job types and right nodes correspond to servers, with each edge indicating that a job type can be served by a server. Thus edges represent locality constraints, i.e., an arbitrary job can only be served at servers which contain certain data and/or machine learning (ML) models. Servers in this system can have heterogeneous service rates. In this setting, we investigate the

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Guliyev, Rustam, Aparajita Haldar, and Hakan Ferhatosmanoglu. "D3-GNN: Dynamic Distributed Dataflow for Streaming Graph Neural Networks." Proceedings of the VLDB Endowment 17, no. 11 (2024): 2764–77. http://dx.doi.org/10.14778/3681954.3681961.

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Graph Neural Network (GNN) models on streaming graphs entail algorithmic challenges to continuously capture its dynamic state, as well as systems challenges to optimize latency, memory, and throughput during both inference and training. We present D3-GNN, the first distributed, hybrid-parallel, streaming GNN system designed to handle real-time graph updates under online query setting. Our system addresses data management, algorithmic, and systems challenges, enabling continuous capturing of the dynamic state of the graph and updating node representations with fault-tolerance and optimal latenc

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Gedvila, Andrius, and Vadim Mokšin. "FEM Investigation of Load Carrying Capacity of Magnetic Vibration Isolating Supports." Solid State Phenomena 251 (July 2016): 94–99. http://dx.doi.org/10.4028/www.scientific.net/ssp.251.94.

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The load carrying capacity of self-centering permanent magnet based vibration isolating support is analyzed in the article. COMSOL Multiphysics® modeling software was used for numerical simulation. Results are presented as repulsion force versus support cap displacement graph as well as magnetic flux density distribution graphs. Repulsion force was also measured experimentally. It is established that differences between numerically and experimentally obtained values are only about 2–8%. It is also established that near-zero stiffness of the support is achieved in case of 70 N normal load force

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Zheng, Yao, Dong Hua Zhou, Zhi Lun Ouyang, Jing Lin Zhu, and Chun Xiu Han. "Nomo-Graph for Determining the Effective Length of Columns with the Leaning Columns." Applied Mechanics and Materials 610 (August 2014): 88–93. http://dx.doi.org/10.4028/www.scientific.net/amm.610.88.

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In this paper the elastic load-carrying capacity of the stability columns, which provide lateral support to leaning columns, is investigated. The solution to the effective length of the columns is obtained by using of differential equations. The influences of the leaning columns on the stabilizing columns are studied and the nomo-graphs for determining effective length of the supporting columns are made for practical using. The effective length of columns can be quickly determined by using the nomo-graph, which shows intuitively the trends of the curves in the nomo-graph owing to some relevant

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Haryanti, Munnik, Adi Nugroho, Bekti Yulianti, and Yohannes Dewanto. "Transfer Function and Transient Response Analysis of Active Inrush Current Limiting Circuit on P-MOSFET with Equivalent RLC Series Circuit Load of Inverter." Ranah Research : Journal of Multidisciplinary Research and Development 6, no. 3 (2024): 293–300. http://dx.doi.org/10.38035/rrj.v6i3.818.

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Inrush current is a transient current surge when a piece of electrical equipment is turned on. One of the solutions is a P-MOSFET-based active inrush current limiter, which will be analyzed with KiCad software to analyze the transient response graph and a Python program to analyze the transfer function graph. From the observation, the circuit could reduce the inrush current amount which was initially ranging from 12mA – 12A to below 500nA, with a very low amount of ripple (under 100nA peak-to-peak). The transfer function graph from the RLC load represents its transient response graph, and the

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DIVAKARAN, UMA, and AMIT DUTTA. "FIBERS ON A GRAPH WITH LOCAL LOAD SHARING." International Journal of Modern Physics C 18, no. 06 (2007): 919–26. http://dx.doi.org/10.1142/s0129183107010632.

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We study a random fiber bundle model with tips of the fibers placed on a graph having co-ordination number 3. These fibers follow local load sharing with uniformly distributed threshold strengths of the fibers. We have studied the critical behavior of the model numerically using a finite size scaling method and the mean field critical behavior is established. The avalanche size distribution is also found to exhibit a mean field nature in the asymptotic limit.

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He, Kanghang, Lina Stankovic, Jing Liao, and Vladimir Stankovic. "Non-Intrusive Load Disaggregation Using Graph Signal Processing." IEEE Transactions on Smart Grid 9, no. 3 (2018): 1739–47. http://dx.doi.org/10.1109/tsg.2016.2598872.

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Boillat, J. E. "Load balancing and Poisson equation in a graph." Concurrency: Practice and Experience 2, no. 4 (1990): 289–313. http://dx.doi.org/10.1002/cpe.4330020403.

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Beyer, F., D. Schneider, and A. Schumacher. "Finding three-dimensional layouts for crashworthiness load cases using the graph and heuristic based topology optimization." Structural and Multidisciplinary Optimization 63, no. 1 (2020): 59–73. http://dx.doi.org/10.1007/s00158-020-02768-0.

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AbstractIn this paper we present a new procedure using the graph and heuristic based topology optimization in order to find layouts for three-dimensional frame structures under crash loads. A three-dimensional graph describes the geometry and is used to derive a finite element shell model. The model of the frame structure consists of different profiles with continuous cross-sections. The ends of the profiles are currently rigidly connected. Each cross-section is defined by an individual two-dimensional graph. After performing a simulation its results are used by competing heuristics to propose

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HOLZRICHTER, MICHAEL, and SUELY OLIVEIRA. "A GRAPH BASED DAVIDSON ALGORITHM FOR THE GRAPH PARTITIONING PROBLEM." International Journal of Foundations of Computer Science 10, no. 02 (1999): 225–46. http://dx.doi.org/10.1142/s0129054199000162.

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The problem of partitioning a graph such that the number of edges incident to vertices in different partitions is minimized, arises in many contexts. Some examples include its recursive application for minimizing fill-in in matrix factorizations and load-balancing for parallel algorithms. Spectral graph partitioning algorithms partition a graph using the eigenvector associated with the second smallest eigenvalue of a matrix called the graph Laplacian. The focus of this paper is the use graph theory to compute this eigenvector more quickly.

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Cai, Shuangyu, Boyu Tian, Huanchen Zhang, and Mingyu Gao. "PimPam: Efficient Graph Pattern Matching on Real Processing-in-Memory Hardware." Proceedings of the ACM on Management of Data 2, no. 3 (2024): 1–25. http://dx.doi.org/10.1145/3654964.

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Graph pattern matching is powerful and widely applicable to many application domains. Despite the recent algorithm advances, matching patterns in large-scale real-world graphs still faces the memory access bottleneck on conventional computing systems. Processing-in-memory (PIM) is an emerging hardware architecture paradigm that puts computing cores into memory devices to alleviate the memory wall issues. Real PIM hardware has recently become commercially accessible to the public. In this work, we leverage the real PIM hardware platform to build a graph pattern matching framework, PimPam, to be

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ELSÄSSER, ROBERT, BURKHARD MONIEN, ROBERT PREIS, and ANDREAS FROMMER. "Optimal Diffusion Schemes and Load Balancing on Product Graphs." Parallel Processing Letters 14, no. 01 (2004): 61–73. http://dx.doi.org/10.1142/s0129626404001714.

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We discuss nearest neighbor load balancing schemes on processor networks which are represented by a cartesian product of graphs and present a new optimal diffusion scheme for general graphs. In the first part of the paper, we introduce the Alternating-Direction load balancing scheme, which reduces the number of load balance iterations by a factor of 2 for cartesian products of graphs. The resulting flow is theoretically analyzed and can be very high for certain cases. Therefore, we further present the Mixed-Direction scheme which needs the same number of iterations but computes in most cases a

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Padilla Garcia, Aaron, Gilberto Gonzalez-Avalos, Gerardo Ayala-Jaimes, Noe Barrera Gallegos, Juancarlos Mendez-B., and David Alvarado-Zamora. "Tracking Control of Physical Systems with Application to a System with a DC Motor: A Bond Graph Approach." Symmetry 14, no. 4 (2022): 755. http://dx.doi.org/10.3390/sym14040755.

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In this paper, the bond graph modeling for the control of tracking systems has been applied. The closed loop system is built by the bond graph model of the system to be controlled, an additional bond graph according to the tracking input signal, and feedback gains in the physical domain. Hence, a procedure to obtain the closed loop tracking system is proposed. The proposal of modeling and tracking control systems in this paper determines symmetries in the bond graph approach with respect to the traditional algebraic approach. The great advantage of this graphical approach is that the mathemati

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Hussein, Rana, Alberto Lerner, Andre Ryser, Lucas David Bürgi, Albert Blarer, and Philippe Cudre-Mauroux. "GraphINC: Graph Pattern Mining at Network Speed." Proceedings of the ACM on Management of Data 1, no. 2 (2023): 1–28. http://dx.doi.org/10.1145/3589329.

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Graph Pattern Mining (GPM) is a class of algorithms that identifies given shapes within a graph, e.g., cliques of a certain size. Any area of a graph can contain a shape of interest, but in real-world graphs, these shapes tend to be concentrated in areas deemed skewed. Because mining skewed areas can dominate GPM computations, the overwhelming majority of state-of-the-art GPM techniques break such areas into many small parts and load balance them across servers. This paper takes a diametrically opposite approach: we suggest a framework that concentrates rather than divides the skewed areas. Ou

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