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ACM SIGAPP Applied Computing Review

Volume 24, Issue 1

March 2024

SIGAPP, ACM's primary applications-oriented SIG, offers practitioners and researchers the opportunity to share mutual interest in innovative applications, technology transfer, experimental computing, strategic research, and the management of computing. This SIG also promotes widespread cooperation among business, government, and academic computing programs.

Areas of Special Interest
Integration of traditional computing disciplines such as graphics, databases, communication, software engineering, artificial intelligence and office automation with emerging technologies such as neural networks, logic and symbolic programming, expert systems, and image information systems.

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Volume 24, Issue 1 March 2024
ISSN:1559-6915
EISSN:1931-0161
View Table of Contents
research-article
Power System Frequency Dynamics Modeling, State Estimation, and Control using Neural Ordinary Differential Equations (NODEs) and Soft Actor-Critic (SAC) Machine Learning Approaches
- Pooja Aslami
  South Dakota State University, Brookings, South Dakota, USA
  ,
- Tara Aryal
  South Dakota State University, Brookings, South Dakota, USA
  ,
- Astha Rai
  University of Maine, Orono, Maine, USA
  ,
- Niranjan Bhujel
  University of Maine, Orono, Maine, USA
  ,
- Hossein Moradi Rekabdarkolaee
  South Dakota State University, Brookings, South Dakota, USA
  ,
- Kaiqun Fu
  South Dakota State University, Brookings, South Dakota, USA
  ,
- Reinaldo Tonkoski
  Technical University of Munich, Munich, Germany
  ,
- Zongjie Wang
  University of Connecticut, Storrs, Connecticut, USA
  ,
- Timothy M. Hansen
  South Dakota State University, Brookings, South Dakota, USA
May 2024pp 24-39 https://doi.org/10.1145/3663652.3663655
research-article
Deep Convolutional Neural Network Compression based on the Intrinsic Dimension of the Training Data
- Abir Mohammad Hadi
  South Dakota State University, Brookings, South Dakota, USA
  ,
- Kwanghee Won
  South Dakota State University, Brookings, South Dakota, USA
May 2024pp 14-23 https://doi.org/10.1145/3663652.3663654
research-article
Finite-Time Consensus of Second-Order Multi-Agent Systems via Event-Triggered Impulsive Control
- Shasha Yang
  School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
  ,
- Ziming Liu
  School of Communications and Information Engineering, Chongqing University of Posts and Telecommunications, Chongqing, China
  ,
- Lianghao Ji
  School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, China
May 2024pp 5-13 https://doi.org/10.1145/3663652.3663653

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Power System Frequency Dynamics Modeling, State Estimation, and Control using Neural Ordinary Differential Equations (NODEs) and Soft Actor-Critic (SAC) Machine Learning Approaches

Deep Convolutional Neural Network Compression based on the Intrinsic Dimension of the Training Data

Finite-Time Consensus of Second-Order Multi-Agent Systems via Event-Triggered Impulsive Control

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