https://scholar.dgist.ac.kr/handle/20.500.11750/10144 2026-08-03T10:12:22Z https://scholar.dgist.ac.kr/handle/20.500.11750/60554 Title: Attention-Based Multimodal Transformer-LSTM Fusion Networks for Enhanced Lithium-ion Battery State-of-Charge Prediction with Aging Pattern Consideration Author(s): HyunKi, Ryu; Cho, SungRae; Jon, YongJun; Kim, Bonghwan; Kim, Dongkyun Abstract: Lithium-ion batteries play a pivotal role in electric vehicles (EVs) and energy storage systems, where accurate State-of-Charge (SoC) prediction is essential for ensuring the efficiency and safety of battery management systems (BMS). This study conducts an in-depth analysis of the temperature factors that most significantly influence battery remaining capacity prediction, with a particular focus on accurately predicting battery SoC variations under extreme temperature conditions ranging from - 30 degrees C to 80 degrees C. The research methodology employs a multimodal neural network that com-bines Transformer architecture, which demonstrates superior performance in processing static characteristic data, with Long Short-Term Memory (LSTM) networks, which exhibit exceptional capabilities in time-series data processing. The model effectively integrates battery temperature performance data with NASA aging datasets through an attention-based fusion approach, enabling efficient information exchange between heterogeneous data modalities. Experimental results demonstrate that the proposed model achieves an MAE of 0.2438 +/- 0.016 on the overall test set across - 30 degrees C to 80 degrees C. Additionally, the model attained an RMSE of 0.3156 +/- 0.020 and an R & sup2; of 0.9501 +/- 0.009, representing a 24.6% improve-ment over baseline LSTM models, and an additional 14% improvement attributable to the attention mechanism when compared to simple concatenation-based fusion. Furthermore, the attention-based fusion approach demonstrates marked performance improvements compared to simple combination methods. These results demonstrate potential applicability to electric vehicles and energy storage systems through comprehensive laboratory validation under diverse temperature conditions (-30 degrees C to 80 degrees C), though field testing is required for deployment verification. 2026-04-30T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60466 Title: Multimodal Remote Heart Rate Estimation via Spatio-Temporal Transformers and Adaptive Fusion Author(s): Kim, Hyunduk; Lee, Sang-Heon; Sohn, Myoung-Kyu; Kim, Junkwang; Park, Hyeyoung Abstract: Remote photoplethysmography (rPPG) enables noncontact heart rate (HR) estimation from facial videos. Despite recent advances, single-modality methods remain vulnerable to motion, illumination changes, and modality-specific degradations. We address these limitations with a multimodal framework that explicitly leverages complementary RGB and infrared (IR, thermal or NIR) streams. Built on a 3-D SwiftFormer backbone, the method integrates three modules: 1) a context-aware temporal difference convolution (CTDC) that amplifies motion-sensitive cues via multiscale temporal differencing; 2) a bidirectional cross-attention (BCA) that enables hierarchical information exchange between modalities; and 3) a cross-modal gating fusion (CMGF) that adaptively combines features using a temperature-scaled logit-difference gate. Training is guided by a hybrid objective over time and frequency, augmented with a scheduled soft-DTW alignment term. Extensive experiments on two public datasets demonstrate consistent improvements over state-of-the-art baselines, with ablation studies confirming the contributions of CTDC, BCA, CMGF, and soft-DTW. These results highlight the effectiveness of explicit cross-modal interaction and adaptive fusion for robust, accurate remote HR (rHR) estimation. 2026-01-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60266 Title: Performance Analysis of Sensor Fusion Models Using Unmanned Ground Vehicle Author(s): Minsu-Jo; Baek, Youngmi; Lee, Jin-Hee; Son, Sang Hyuk Abstract: In this paper, we analyze the performance of various sensor fusion models using an unmanned ground vehicle. In the given attack scenarios, we examine how the attacks influence on each fusion model by comparing the results of the models. We conduct the experiments with real measurement data obtained from an unmanned ground vehicle. © 2016 IEEE. 2015-12-31T15:00:00Z https://scholar.dgist.ac.kr/handle/20.500.11750/60265 Title: RemCare-Remote Caregiver Using Integrated Framework for People with Cognitive Disability Author(s): Lee, Jin-Hee; Son, Sang Hyuk Abstract: In recent years, people with cognitive disability has increased rapidly. The increase of them considerably affects the family and society that interact with the cognitively disabled person. Children with developmental disability or patients with dementia need to monitor and track their behaviors for their safety. We design smart tracking system which effectively can help to trace their location and to monitor their activities. The system applies a wireless sensor network with an efficient sensor deployment method and an adaptive packet scheduling algorithm. In addition, it provides the real-time monitoring to observe their situation and gives a quick alerting to them and caregivers. In this paper, we present an overview of an integrated framework. © 2016 IEEE. 2015-12-31T15:00:00Z