ANALISIS PENYEBARAN KASUS COVID-19 MENGGUNAKAN GRAPH SIGNAL PROCESSING: STUDY KASUS DI KOTA KUPANG, INDONESIA DOI Open Access
Amin Ajaib Maggang,

Sarlince O. Manu,

Beby H. A. Manafe

и другие.

Media Elektro, Год журнала: 2023, Номер unknown, С. 111 - 119

Опубликована: Окт. 30, 2023

Untuk memberikan informasi penyebaran kasus Covid-19, Pemerintah Kota Kupang, Provinsi Nusa Tenggara Timur, telah membuat peta Covid-19 melalui website pemerintah Kota. Namun, seperti kebanyakan terkait tersebut hanya jumlah harian. Informasi kelurahan yang berisiko terjadi lonjakan atau berpotensi menyebarkan ke lain belum terdata di laman web tersebut. Oleh karena itu, penelitian ini bertujuan untuk gambaran teknik analisis sehingga menghasilkan lebih detail mengenai pola penularannya. Penelitian menggunakan Graph Signal Processing (GSP) menganalisis berdasarkan struktur graph menghubungkan 51 kecamatan Kupang. Berbeda dengan metode data analysis lain, GSP mampu mempertimbangkan relasi antara objek, dalam hal jarak antar kelurahan. Data digunakan adalah tercatat pada tanggal 6 bulan Maret tahun 2021. Hasil menunjukkan bahwa dapat mengidentifikasi tinggi mengalami kasus, yaitu Nunleu dan menjadi sumber outbreaks Airnona.

Real-time water quality prediction in water distribution networks using graph neural networks with sparse monitoring data DOI
Zilin Li,

Haixing Liu,

Chi Zhang

и другие.

Water Research, Год журнала: 2023, Номер 250, С. 121018 - 121018

Опубликована: Дек. 14, 2023

Язык: Английский

Процитировано

44

Graph Filters for Signal Processing and Machine Learning on Graphs DOI
Elvin Isufi, Fernando Gama, David I Shuman

и другие.

IEEE Transactions on Signal Processing, Год журнала: 2024, Номер 72, С. 4745 - 4781

Опубликована: Янв. 1, 2024

Filters are fundamental in extracting information from data. For time series and image data that reside on Euclidean domains, filters the crux of many signal processing machine learning techniques, including convolutional neural networks. Increasingly, modern also networks other irregular domains whose structure is better captured by a graph. To process learn such data, graph account for underlying domain. In this article, we provide comprehensive overview filters, different filtering categories, design strategies each type, trade-offs between types filters. We discuss how to extend into filter banks enhance representational power; is, model broader variety classes, patterns, relationships. showcase role applications. Our aim article provides unifying framework both beginner experienced researchers, as well common understanding promotes collaborations at intersections processing, learning, application domains.

Язык: Английский

Процитировано

35

A review of graph and complex network theory in water distribution networks: Mathematical foundation, application and prospects DOI

Xipeng Yu,

Yipeng Wu, Fanlin Meng

и другие.

Water Research, Год журнала: 2024, Номер 253, С. 121238 - 121238

Опубликована: Фев. 3, 2024

Язык: Английский

Процитировано

17

Shall we always use hydraulic models? A graph neural network metamodel for water system calibration and uncertainty assessment DOI
Ariele Zanfei, Andrea Menapace, Bruno Brentan

и другие.

Water Research, Год журнала: 2023, Номер 242, С. 120264 - 120264

Опубликована: Июнь 24, 2023

Язык: Английский

Процитировано

21

Resilience evaluation for water distribution system based on partial nodes’ hydraulic information DOI

Xipeng Yu,

Yipeng Wu, Xiao Zhou

и другие.

Water Research, Год журнала: 2023, Номер 241, С. 120148 - 120148

Опубликована: Май 30, 2023

Язык: Английский

Процитировано

19

Towards transferable metamodels for water distribution systems with edge-based graph neural networks DOI Creative Commons
Bulat Kerimov, Riccardo Taormina, Franz Tscheikner-Gratl

и другие.

Water Research, Год журнала: 2024, Номер 261, С. 121933 - 121933

Опубликована: Июнь 20, 2024

Data-driven metamodels reproduce the input-output mapping of physics-based models while significantly reducing simulation times. Such techniques are widely used in design, control, and optimization water distribution systems. Recent research highlights potential based on Graph Neural Networks as they efficiently leverage graph-structured characteristics Furthermore, these possess inductive biases that facilitate generalization to unseen topologies. Transferable particularly advantageous for problems require an efficient evaluation many alternative layouts or when training data is scarce. However, transferability GNNs remains limited, due lack representation physical processes occur edge level, i.e. pipes. To address this limitation, our work introduces Edge-Based Networks, which extend set represent link-level more detail than traditional Networks. architecture theoretically related constraints mass conservation at junctions. verify approach, we test suitability edge-based network estimate pipe flowrates nodal pressures emulating steady-state EPANET simulations. We first compare effectiveness several benchmark systems against Then, explore by evaluating performance For each configuration, calculate model metrics, such coefficient determination speed-up with respect original numerical model. Our results show proposed method captures pipe-level accurately node-based models. When tested networks a similar demands, retains good up 0.98 0.95 predicted heads. Further developments could include simultaneous derivation flowrates.

Язык: Английский

Процитировано

6

Online state estimation in water distribution systems via Extended Kalman Filtering DOI
Matthew Bartos, Meghna Thomas, M. H. Kim

и другие.

Water Research, Год журнала: 2024, Номер 264, С. 122201 - 122201

Опубликована: Авг. 5, 2024

Язык: Английский

Процитировано

5

GraphSmart: a method for green and accurate IoT water monitoring DOI Open Access
Tiziana Cattai, Stefania Colonnese, Domenico Garlisi

и другие.

ACM Transactions on Sensor Networks, Год журнала: 2024, Номер 20(6), С. 1 - 32

Опубликована: Окт. 18, 2024

Water scarcity is nowadays a critical global concern and an efficient management of water resources paramount. This paper presents original approach for monitoring Distribution Systems (WDSs) through Internet Things (IoT) that involves the integration multiple sensors placed across distribution network to accurately measure flow. To enhance energy efficiency green communication process, we harness power graph theory signal processing represent in tunable accurate way flow simultaneously minimize number IoT communicating those measurements. We propose model where represented as on introduce algorithm, named GraphSmart, designed reconstruct when certain measurements are unknown or missing. Our framework applied synthetic realistic environment within context LoRaWAN (Long Range Wide Area Network), infrastructure protocol ultra-low-power devices. findings show GraphSmart significantly reduces consumption while ensuring precise estimation. research demonstrates high potential energy-efficient monitoring, paving improve WDSs enabling operators address challenges.

Язык: Английский

Процитировано

3

Spatiotemporal graph convolutional network using sparse monitoring data for accurate water-level reconstruction in urban drainage systems DOI
Li He,

Jun Nan,

Lei Chen

и другие.

Journal of Hydrology, Год журнала: 2025, Номер unknown, С. 132681 - 132681

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0

A novel approach based on graph signal processing and sampling theory to set pressure sensors in water distribution networks DOI
Daniel Barros, Carlo Giudicianni, Enrico Creaco

и другие.

Expert Systems with Applications, Год журнала: 2025, Номер unknown, С. 126306 - 126306

Опубликована: Янв. 1, 2025

Язык: Английский

Процитировано

0