Search Results for author: Jianwu Wang

Found 5 papers, 2 papers with code

An Edge-Cloud Integrated Framework for Flexible and Dynamic Stream Analytics

no code implementations10 May 2022 Xin Wang, Azim Khan, Jianwu Wang, Aryya Gangopadhyay, Carl E. Busart, Jade Freeman

In this paper, we study how to best leverage edge and cloud resources to achieve better accuracy and latency for stream analytics using a type of RNN model called long short-term memory (LSTM).

Edge-computing object-detection +2

Reproducible and Portable Big Data Analytics in the Cloud

1 code implementation17 Dec 2021 Xin Wang, Pei Guo, Xingyan Li, Jianwu Wang, Aryya Gangopadhyay, Carl E. Busart, Jade Freeman

We propose and develop an open-source toolkit that supports 1) fully automated end-to-end execution and reproduction via a single command, 2) automatic data and configuration storage for each execution, 3) flexible client modes based on user preferences, 4) execution history query, and 5) simple reproduction of existing executions in the same environment or a different environment.

Sea Ice Forecasting using Attention-based Ensemble LSTM

1 code implementation27 Jul 2021 Sahara Ali, Yiyi Huang, Xin Huang, Jianwu Wang

Accurately forecasting Arctic sea ice from subseasonal to seasonal scales has been a major scientific effort with fundamental challenges at play.

Scalable and Hybrid Ensemble-Based Causality Discovery

no code implementations24 Dec 2020 Pei Guo, Achuna Ofonedu, Jianwu Wang

Causality discovery mines cause-effect relationships among different variables of a system and has been widely used in many disciplines including climatology and neuroscience.

Distributed Computing Time Series

A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification

no code implementations24 Aug 2018 Wenbin Zhang, Jianwu Wang, Daeho Jin, Lazaros Oreopoulos, Zhibo Zhang

A self-organizing map (SOM) is a type of competitive artificial neural network, which projects the high-dimensional input space of the training samples into a low-dimensional space with the topology relations preserved.

General Classification

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