Daegu Gyeongbuk Institute of Science and Technology
DGIST Library Institutional RepositoryNot a member yet
12664 research outputs found
Sort by
APPARATUS AND METHOD FOR GENERATING SEMI-SUPERVISED FSS MODEL FOR UNCERTAINTY PREDICTION
No full textNovel Acceleration Estimation for Improving Stability and Performance in Impedance Control
No full textThis article presents a novel approach to enhancing the stability and performance of impedance control in robotic systems through improved acceleration estimation. The focus is on Series Elastic Actuators (SEAs), which are vital for achieving precise force control and ensuring safe and efficient human-robot collaboration. Traditional cascade impedance control is limited by the passive nature of the system, particularly when rendering high stiffness values. To address these limitations, we introduce Elastic Structure Preserving (ESP) control, which retains the inherent elastic structure of the SEA while enabling high-performance impedance rendering. A key aspect of ESP is the accurate estimation of load-side acceleration, which is crucial for ensuring stability and maintaining control performance. To achieve this, we propose a new acceleration estimation technique that enhances impedance rendering. Experimental results demonstrate the effectiveness of the proposed method, which shows improved stability and precise impedance control. This approach offers a robust solution for advanced robotic applications requiring high stiffness and precise force control. © ICROS 2024.FALSEscopuskc
Microstructure-based digital twin thermo-electrochemical modeling of LIBs at the cell-to-module scale
No full textAs the application of lithium-ion batteries (LIBs) expands beyond conventional electric vehicles (EVs) to heavy vehicles such as electric trucks or trams, the importance of thermal management in LIB systems is increasing, even at the module or pack level. In particular, because monitoring the thermal behaviors of each cell is not feasible, thermo-electrochemical modeling and simulations in the module or pack level are essential for analyzing and ensuring thermal stability. However, because the conventional lumped thermo-electrochemical models cannot reflect the actual structure of LIB cells, there might be considerable differences may exist between simulation and experimental results. To fill these gaps, we have newly developed a 3D microstructure-based digital twin model of a battery module (8.8 Ah/18.5 V, five LIB pouch cells in series) for an unmanned railway vehicle. Unlike traditional lumped models, our digital twin model accurately well reflects the internal structure of cells and can calculate the heat generation of each component inside a cell. As a result, contrary to a lumped model, the digital twin model can not only simulate the inhomogeneous temperature gradient inside a cell, but also estimates higher local maximum temperatures (TDT, max/TL, max = 137.2 °C/123.9 °C @ 10C discharge) in cells which can trigger thermal runaway. Therefore, microstructure-based digital twin modeling can alleviate concerns regarding the thermal runaway of LIB cells, modules, and packs, and provide safe operating conditions. © 2024 Elsevier B.V.FALSEsciescopu
Dynamic Network Slicing Control Framework in AI-Native Hierarchical Open-RAN Architecture
No full textNetwork slicing is a promising technology in next-generation wireless networks that enables the division of a physical network infrastructure into multiple virtual networks, each of which is tailored for specific service requirements. This approach enables a more flexible allocation of network resources such as beamforming vector, bandwidth and transmit power; thereby effectively supporting services that require high data transmission rates. However, in dynamic network environments where multiple users dynamically move around; hence the interference relationships are dynamically varying, traditional static network slicing solution has critical drawbacks. To this end, for the effective implementation and performance improvement in practical and dynamic network environments, we first propose a dynamic network slicing control framework in AI-native hierarchical Open-RAN (Radio Access Network) architecture where mobility prediction and network controls are designed by multiple timescale decomposition. The proposed framework can facilitate effective network controls, enabling the generation of finely tuned QoS management decisions (power/ bandwidth allocation, user scheduling, beam activation) in different timescales. On top of this framework, we compare the performance of a simple dynamic network slicing algorithm and an existing static network slicing scheme via simulations. © 2024 IEEE
ELECTRODE FOR THIN-FILM SOLAR CELL BASED ON MESH STRUCTURE AND MANUFACTURING METHOD THERE OF
No full textHydroquinone-treated Cu3(BTC)2: a mixed-valence Cu(i/ii) MOF catalyst for efficient cycloadditions
No full textWe present mixed-valence Cu(I)1Cu(II)2(BTC)2 [henceforth Cu(I/II)-HKUST-1], post-synthetically prepared via the hydroquinone (H2Q) treatment of Cu(II)3(BTC)2 (also referred to as HKUST-1) and its subsequent catalytic activity.This Cu(I/II)-HKUST-1 exhibits exceptional structural integrity and superior catalytic performance in the copper-catalyzed azide-alkyne cycloaddition (CuAAC) reaction between phenylacetylene and benzyl azide.These findings highlight the potential of mixed-valence Cu-based MOFs as effective and sustainable heterogeneous catalysts for organic transformations, paving the way for future advancements in MOF-based catalysis. © The Royal Society of Chemistry 2024.FALSEsciescopu
Dual-functional metal-organic framework for chemisorption and colorimetric monitoring of cyanogen chloride
No full textGiven the growing concern over the deployment of toxic chemicals in warfare, the rapid and accurate removal and detection of cyanogen chloride (CK) as a blood agent has become increasingly critical. However, conventional physisorbents and chemisorbents used in military respirators are insufficient for the effective removal of CK. In this study, we demonstrate the chemisorption and sensing abilities of Co2(m-DOBDC) (m-DOBDC4− = 4,6-dioxo-1,3-benzenedicarboxylate) for CK via electrophilic aromatic substitution (EAS) in humid environments. Unlike the chemisorption in triethylenediamine (TEDA) impregnated carbon materials, which generates by-products through hydrolysis, the electron-rich C5 sites in m-DOBDC4− ligands give rise to cyano substitution with CK. This leads to the formation of stable C–C bonds and chloride ions (Cl−) coordinating with open Co2+ sites. Such a mechanism prevents the generation of toxic by-products like cyanic acid and hydrochloric acid. Breakthrough experiments conducted in a packed-bed system conclusively demonstrated the superior CK removal capacity of Co2(m-DOBDC) (1662 min/g), compared to TEDA-impregnated activated carbon (323 min/g) under humid conditions. Considering that MOF-74 series, isostructural with Co2(m-DOBDC), barely adsorb CK under similar conditions, this finding marks a significant advancement in developing novel sorbents for CK removal. Moreover, this chemisorption not only exhibited rapid and highly efficient CK removal but also enabled colorimetric monitoring via the distinctive color change induced by the coordination of Cl− acting as σ donors. These findings facilitate the development of adsorption and sensing equipment to protect military personnel from toxic chemical threats. © 2024 Elsevier LtdFALSEsciescopu
Insights into Translocation of Arginine-Rich Cell-Penetrating Peptides across a Model Membrane
No full textIt is well-known that membrane deformation and water pores contribute to the spontaneous translocation of arginine-rich cell-penetrating peptides (CPPs). We confirm this through the observation of the spontaneous translocation of single R9 (nona-arginine) and Tat (48-60) peptides across a model membrane using the weighted ensemble (WE) method within all-atom molecular dynamics (MD) simulations. Furthermore, we demonstrate that membrane deformation and the presence of a water pore reduce the effective charge of the CPP and the bending rigidity of the model membrane during translocation. We find that R9 disturbs the model membrane more than Tat (48-60), leading to more efficient translocation of R9 across the model membrane. © 2024 American Chemical Society.FALSEsciescopu
BEV Image-based Lane Tracking Control System for Autonomous Lane Repainting Robot
No full textIn this paper, we present a novel study on a BEV (bird's eye view) image-based lane tracking control system for an autonomous lane repainting robot. Our research introduces a cutting-edge lane detection method based on BEV images, leveraging row-anchor techniques to enhance precision and provide detailed error information for lane tracking algorithms. By utilizing real-time sensor data and advanced deep learning processes, we have successfully implemented a high-performance lane repainting system that minimizes errors and ensures accuracy. Our proposed position-based visual pure pursuit algorithm (PV-PP) plays a crucial role in guiding the lane repainting process with precision and efficiency, ultimately improving the functionality and feasibility of the linear actuator responsible for paint spraying in the real indusrial fields. Through our contributions, including innovative lane detection methods, real-time sensor utilization, and robot control algorithm design, we aim to advance the field of autonomous lane repainting robots and enhance the safety and effectiveness of road maintenance operations. © 2024 IEEE
Development of Deep Learning-Based Artifact Removal Method for Deep Brain Stimulation
No full text뇌심부자극 (Deep Brain Stimulation, DBS)은 파킨슨병 치료법 중 하나로, DBS 와 동시에 뇌신호를 기록할 경우 자극 아티팩트로 인해 신호가 오염되어 기록되는 문제가 발생한다. 이에 오염된 신호를 복원하는 과정이 필요하며, 이를 위해 효과적인 자극 아티팩트 제거 (Stimulation Artifact Cancellation, SAC) 기술이 요구된다. 기존의 선형 기반 SAC 기법은 아티팩트가 비사인모양 파형을 갖거나 시간에 따라 달라지는 파형을 가질 경우 아티팩트를 제거하는 데 한계를 가진다. 본 기법에서는 이를 해결하기 위해 입력 신호의 다중 해상도 분석 (Multi-Resolution Analysis, MRA)을 학습 데이터로 사용하는 Convolutional Neural Network (CNN) 구조 기반의 SAC 기법을 제안한다. 제안 기법의 효과를 평가하기 위해 시뮬레이션을 통해 데이터를 생성하였으며, 결과적으로 제안 기법은 시간 및 주파수 영역의 정보를 모두 학습함으로써 자극 아티팩트의 세기가 달라지는 상황에서도 기존 알고리즘 대비 원래의 뇌신호를 거의 손상 없이 복원하는 성능을 나타낸다. 본 제안 기법은 뇌신호를 기록 및 자극하는 각종 뇌-컴퓨터 인터페이스 발전에 기여할 것으로 기대된다