Digital Library
Search: "[ keyword: LSTM ]" (12)
Gear Fault Diagnosis Based on Residual Patterns of Current and Vibration Data by Collaborative Robot’s Motions Using LSTM
Baek Ji Hoon, Yoo Dong Yeon, Lee Jung Won KIPS Transactions on Software and Data Engineering,
Vol. 12, No. 10, pp. 445-454,
Oct.
2023
https://doi.org/10.3745/KTSDE.2023.12.10.445
Keywords: Gear Fault Diagnosis, Correlation Analysis, LSTM, Collaborative Robot, Prognostics and Health Management
https://doi.org/10.3745/KTSDE.2023.12.10.445
Keywords: Gear Fault Diagnosis, Correlation Analysis, LSTM, Collaborative Robot, Prognostics and Health Management
Towards Carbon-Neutralization: Deep Learning-Based Server Management Method for Efficient Energy Operation in Data Centers
Sang-Gyun Ma, Jaehyun Park, Yeong-Seok Seo KIPS Transactions on Software and Data Engineering,
Vol. 12, No. 4, pp. 149-158,
Apr.
2023
https://doi.org/10.3745/KTSDE.2023.12.4.149
Keywords: data center, Deep Learning, LSTM, Quality of Service, Server Management, traffic prediction
https://doi.org/10.3745/KTSDE.2023.12.4.149
Keywords: data center, Deep Learning, LSTM, Quality of Service, Server Management, traffic prediction
Comparison of Deep Learning Models Using Protein Sequence Data
Jeung Min Lee, Hyun Lee KIPS Transactions on Software and Data Engineering,
Vol. 11, No. 6, pp. 245-254,
Jun.
2022
https://doi.org/10.3745/KTSDE.2022.11.6.245
Keywords: CNN, LSTM, GRU, Combined Model, Protein Sequence
https://doi.org/10.3745/KTSDE.2022.11.6.245
Keywords: CNN, LSTM, GRU, Combined Model, Protein Sequence
Understanding the Association Between Cryptocurrency Price Predictive Performance and Input Features
Jaehyun Park, Yeong-Seok Seo KIPS Transactions on Software and Data Engineering,
Vol. 11, No. 1, pp. 19-28,
Jan.
2022
https://doi.org/10.3745/KTSDE.2022.11.1.19
Keywords: LSTM, Deep Learning, Input Feature, Cryptocurrency, Price Prediction, Data Analysis
https://doi.org/10.3745/KTSDE.2022.11.1.19
Keywords: LSTM, Deep Learning, Input Feature, Cryptocurrency, Price Prediction, Data Analysis
Fall Detection Based on 2-Stacked Bi-LSTM and Human-Skeleton Keypoints of RGBD Camera
Shin Byung Geun, Kim Uung Ho, Lee Sang Woo, Yang Jae Young, Kim Wongyum KIPS Transactions on Software and Data Engineering,
Vol. 10, No. 11, pp. 491-500,
Nov.
2021
https://doi.org/10.3745/KTSDE.2021.10.11.491
Keywords: Fall Detection, deep-learning, Skeleton Keypoints, Stacked Bi-LSTM
https://doi.org/10.3745/KTSDE.2021.10.11.491
Keywords: Fall Detection, deep-learning, Skeleton Keypoints, Stacked Bi-LSTM
Leased Line Traffic Prediction Using a Recurrent Deep Neural Network Model
In-Gyu Lee, Mi-Hwa Song KIPS Transactions on Software and Data Engineering,
Vol. 10, No. 10, pp. 391-398,
Oct.
2021
https://doi.org/10.3745/KTSDE.2021.10.10.391
Keywords: Leased Line, Traffic Modeling, time series analysis, Deep Learning, RNN, LSTM
https://doi.org/10.3745/KTSDE.2021.10.10.391
Keywords: Leased Line, Traffic Modeling, time series analysis, Deep Learning, RNN, LSTM
LSTM(Long Short-Term Memory)-Based AbnormalBehavior Recognition Using AlphaPose
Hyun-Jae Bae, Gyu-Jin Jang, Young-Hun Kim, Jin-Pyung Kim KIPS Transactions on Software and Data Engineering,
Vol. 10, No. 5, pp. 187-194,
May.
2021
https://doi.org/10.3745/KTSDE.2021.10.5.187
Keywords: Safety management, Action Recognition, Pose Estimation, LSTM, Deep Learning
https://doi.org/10.3745/KTSDE.2021.10.5.187
Keywords: Safety management, Action Recognition, Pose Estimation, LSTM, Deep Learning
Predicting Win-Loss of League of Legends Using Bidirectional LSTM Embedding
Cheolgi Kim, Soowon Lee KIPS Transactions on Software and Data Engineering,
Vol. 9, No. 2, pp. 61-68,
Feb.
2020
https://doi.org/10.3745/KTSDE.2020.9.2.61
Keywords: League of Legends, Win-Loss Prediction, Machine Learning, Neural Network, LSTM
https://doi.org/10.3745/KTSDE.2020.9.2.61
Keywords: League of Legends, Win-Loss Prediction, Machine Learning, Neural Network, LSTM
Topic Analysis of the National Petition Site and Prediction of Answerable Petitions Based on Deep Learning
Woo Yun Hui, Hyon Hee Kim KIPS Transactions on Software and Data Engineering,
Vol. 9, No. 2, pp. 45-52,
Feb.
2020
https://doi.org/10.3745/KTSDE.2020.9.2.45
Keywords: National Petition, Topic Analysis, topic modeling, K-Means Clustering, LSTM, Deep Learning
https://doi.org/10.3745/KTSDE.2020.9.2.45
Keywords: National Petition, Topic Analysis, topic modeling, K-Means Clustering, LSTM, Deep Learning
Automatic Word Spacing of the Korean Sentences by Using End-to-End Deep Neural Network
Hyun Young Lee, Seung Shik Kang KIPS Transactions on Software and Data Engineering,
Vol. 8, No. 11, pp. 441-448,
Nov.
2019
https://doi.org/10.3745/KTSDE.2019.8.11.441
Keywords: Syllable Embedding, Bi-LSTM, Feedforward Neural Network, Neural Network Language Model, Linear-Chain CRF
https://doi.org/10.3745/KTSDE.2019.8.11.441
Keywords: Syllable Embedding, Bi-LSTM, Feedforward Neural Network, Neural Network Language Model, Linear-Chain CRF
Indexing
All publications of KTSDE is indexed in DOI, EBSCO, Google Scholar, Crossref, and CrossCheck.
KTSDE is also selected as the Journal for Accreditation by NRF (National Research Foundation of Korea).
