DSpace Collection:
http://hdl.handle.net/10203/45
2024-03-19T05:18:48ZMulticlass autoencoder-based active learning for sensor-based human activity recognition
http://hdl.handle.net/10203/314548
Title: Multiclass autoencoder-based active learning for sensor-based human activity recognition
Authors: Park, Hyunseo; Lee, Gyeong Ho; Han, Jaeseob; Choi, Jun Kyun
Abstract: Leveraging the enormous amounts of real-world data collected through Internet of Things (IoT) technologies, human activity recognition (HAR) has become a crucial component of numerous human-centric applications, with the aim of enhancing the quality of human life. While the recent advancements in deep learning have significantly improved HAR, the process of labeling data continues to remain a significant challenge due to the substantial costs associated with human annotation for supervised model training. Active learning (AL) addresses this issue by strategically selecting informative samples for labeling during model training, thereby enhancing model performance. Although numerous approaches have been proposed for sample selection, which consider aspects of uncertainty and representation, the difficulties in estimating uncertainty and exploiting distribution of high-dimensional data still pose a major issue. Our proposed deep learning-based active learning algorithm, called Multiclass Autoencoder-based Active Learning (MAAL), learns latent representation leveraging the capacity of Deep Support Vector Data Description (Deep SVDD). With the multiclass autoencoder which learns the normal characteristics of each activity class in the latent space, MAAL provides an informative sample selection for model training by establishing a link between the HAR model and the selection model. We evaluate our proposed MAAL using two publicly available datasets. The performance results demonstrate the improvements across the overall active learning rounds, achieving enhancements up to 3.23% accuracy and 3.67% in the F1 score. Furthermore, numerical results and analysis of sample selection are presented to validate the effectiveness of the proposed MAAL compared to the alternative comparison methods.2024-02-01T00:00:00ZIntelligent block copolymer self-assembly towards IoT hardware components
http://hdl.handle.net/10203/318012
Title: Intelligent block copolymer self-assembly towards IoT hardware components
Authors: Yang, Geon Gug; Choi, Hee Jae; Li, Sheng; Kim, Jang Hwan; Kwon, Kyeongha; Jin, Hyeong Min; Kim, Bong Hoon; Kim, Sang Ouk2024-02-01T00:00:00ZSatellite Clustering for Non-Terrestrial Networks: Orbital Configuration-Dependent Outage Analysis
http://hdl.handle.net/10203/318349
Title: Satellite Clustering for Non-Terrestrial Networks: Orbital Configuration-Dependent Outage Analysis
Authors: Jung, Dong-Hyun; Ryu, Joon-Gyu; Choi, Junil
Abstract: This letter considers a downlink satellite communication system where a satellite cluster, i.e., a satellite swarm consisting of one leader and multiple follower satellites, serves a ground terminal. The satellites in the cluster form either a linear or circular formation moving in a group and cooperatively sending their signals by maximum ratio transmission precoding. We first conduct a coordinate transformation to effectively capture the relative positions of satellites in the cluster. Next, we derive an exact expression for the orbital configuration-dependent outage probability under the Nakagami fading by using the distribution of the sum of independent Gamma random variables. In addition, we obtain a simpler approximated expression for the outage probability with the help of second-order moment-matching. We also analyze asymptotic behavior in the high signal-to-noise ratio regime and the diversity order of the outage performance. Finally, we verify the analytical results through Monte Carlo simulations. Our analytical results provide the performance of satellite cluster-based communication systems based on specific orbital configurations, which can be used to design reliable satellite clusters in terms of cluster size, formation, and orbits.2024-02-01T00:00:00ZSimilar but Different: A Survey of Ground Segmentation and Traversability Estimation for Terrestrial Robots
http://hdl.handle.net/10203/318467
Title: Similar but Different: A Survey of Ground Segmentation and Traversability Estimation for Terrestrial Robots
Authors: Lim, Hyungtae; Oh, Minho; Lee, Seungjae; Ahn, Seunguk; Myung, Hyun2024-02-01T00:00:00Z