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Knowledge Graph: Ideas saved as nodes and edges connected to other ideas. It creates a network of information that mirrors the way knowledge is stored in the brain. It facilitates deeper understanding, discovery of new connections and efficient retrieval of related concepts.

*** Publication Alert *** I am happy to share that our paper “Physics-aware Regression for DER Dispatch with Topological Reconfigurations of Radial Feeder” got accepted recently in IEEE Transactions on Industry Applications in the special issue on “Convergence of Data-driven and Physics-based Approaches in Power System Analysis, Optimization, and Control”. In this paper, we proposed a physics-aware multi-stage regression-based prediction algorithm for DER dispatch in the presence of different operational bounds, and measurement uncertainties while showing scalability in terms of system size and data volume. The tractability of regression formulation and the embedded learning of network physics show the advantage of applying this technique in real time and implementing the prediction mechanism for even new untrained topologies of the feeder without requiring repetitive training. I am grateful to my co-author Salman Nazir, PhD, and my PhD advisor Aranya Chakrabortty for their valuable feedback during this work at #NCStateUniversity. Last but not the least, I am thankful to Shalini Ghosh, and Suman Majumder for sharing some interesting ideas on data visualization which enhanced the appearance of the results. Paper Link:

I am excited to share our latest publication, titled "Effective Graph-Neural-Network Based Models for Discovering Structural Hole Spanners in Large-Scale and Diverse Networks", in the Expert Systems with Applications Journal (#ImpactFactor: 8.5)! In this study, we introduce two #GraphNeuralNetwork based models designed to identify #CriticalStructuralHoleSpanner nodes in large-scale and diverse networks. These models boast high accuracy while efficiently managing computational resources. Our findings offer significant insights into network analysis and optimization, with potential applications across various domains. Read the full article here: https://lnkd.in/gm5kSWSt Grateful to my supervisors, Dr. Mingyu Guo, Prof. Hong Shen, and A/Prof. Hui Tian, for their invaluable guidance and support. #GraphNeuralNetworks #MachineLearning #NetworkAnalysis

Dear colleagues and fellow researchers, I have opened a "Special Issue" as a "Guest Editor" with Professor (Dr.) Yongbo Li, Professor (Dr.) Bing Li and Dr. Teng Wang in the "Sensors (SCIE, IF: 3.9)" journal. The topic of the special issue is "Advanced Fault Diagnosis and Health Monitoring Techniques for Complex Engineering Systems: 2nd Edition". The first edition of this special issue was hugely successful which motivated us to propose a second version. If you are interested to submit you paper in this special issue, you can do so by clicking the link attached in the comment section. In case of any other query, please do not hesitate to get in touch with me. #Structural_Health_Monitoring #Condition_Monitoring #Reliability_Engineering

📃Scientific paper: Feature-aware ultra-low dimensional reduction of real networks Abstract: In existing models and embedding methods of networked systems, node features describing their qualities are usually overlooked in favor of focusing solely on node connectivity. This study introduces $FiD$-Mercator, a model-based ultra-low dimensional reduction technique that integrates node features with network structure to create $D$-dimensional maps of complex networks in a hyperbolic space. This embedding method efficiently uses features as an initial condition, guiding the search of nodes' coordinates towards an optimal solution. The research reveals that downstream task performance improves with the correlation between network connectivity and features, emphasizing the importance of such correlation for enhancing the description and predictability of real networks. Simultaneously, hyperbolic embedding's ability to reproduce local network properties remains unaffected by the inclusion of features. The findings highlight the necessity for developing network embedding techniques capable of exploiting such correlations to optimize both network structure and feature association jointly in the future. Continued on ES/IODE ➡️ https://etcse.fr/D4be ------- If you find this interesting, feel free to follow, comment and share. We need your help to enhance our visibility, so that our platform continues to serve you.

I'm pleased to announce that our paper titled "A Large Reservoir Computing Forecasting Method Based on Randomized Fuzzy Cognitive Maps" has been published at the IEEE EAIS 2024 conference in Madrid, Spain. This research introduces an updated frame of randomized HFCM (R-HFCM) termed LR-HFCM (Large Reservoir of R-HFCM). The internal layer comprises a large reservoir, which employs various combinations of concepts and orders with a certain number of sub-reservoirs. This frame is designed to effectively capture the diverse dynamics of the input time series. Our results highlight the competitive performance of LR-HFCM compared to various baseline models across 15 different time series. This research was conducted by Omid Orang, Fabricio J. Erazo-Costa, Petrônio Silva, Guilherme Barreto, and Frederico Gadelha Guimarães.  If you are interested, check out the full paper here: https://lnkd.in/dXcps4Av

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