JICS

1. Federated learning-based client training acceleration method for personalized digital twins

YoungHwan Jeong, Won-gi Choi, Hyoseon Kye, JeeHyeong Kim, Min-hwan Song, Sang-shin Lee, Vol. 25, No. 4, pp. 23-37, Aug. 2024

10.7472/jksii.2024.25.4.23
Keywords: Digital Twin, Federated learning, Vector database, training optimization, Privacy, similarity search

2. Efficient distributed consensus optimization based on patterns and groups for federated learning

Seung Ju Kang, Ji Young Chun, Geontae Noh, Ik Rae Jeong, Vol. 23, No. 4, pp. 73-85, Aug. 2022

10.7472/jksii.2022.23.4.73
Keywords: Federated learning, Optimization, Weight model, Communication time, Privacy, ADMM

3. Drone Force Deployment Optimization Algorithm For Efficient Military Drone Operations

Ju-Young Song, Hyeon-Deok Jang, Jong-Moon Chung, Vol. 21, No. 1, pp. 211-219, Feb. 2020

10.7472/jksii.2020.21.1.211
Keywords: Internet of Things, Drone, Internet of Drones, Drone force, Deployment Optimization

4. A Method for Dynamic Clustering-based Efficient Management in Large-Scale IoT Environment

Dae Young Kim, Hyun Jung La, Vol. 15, No. 6, pp. 85-98, Dec. 2014

10.7472/jksii.2014.15.6.85
Keywords: IoT, Dynamic Clustering, Hierarchical Algorithm, Optimization, IoT Computing Environment

5. Bi-directional Fuzzy Matching Algorithm

Hyoung-Rae Kim, In-Soo Jeong, Vol. 12, No. 3, pp. 69-76, Jun. 2011
Keywords: Matching optimization, Bi-directional fuzzy matching algorithm, Employment information matching

6. Classification and Analysis of Mobility Patterns in Nested NEMO Network

Hyung-Jin Lim, Tai-Myoung Chung, Vol. 9, No. 4, pp. 29-42, Aug. 2008

7. An Assignment Motion to Suppress the Unnecessary Code Motion

Hyun-Deok Shin, Dae-Sik Lee, Heui-Hak Ahn, Vol. 9, No. 1, pp. 55-68, Feb. 2008
Keywords: code optimization, assignment motion, code motion

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