Daegu Gyeongbuk Institute of Science and Technology
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ScaleCache: A Scalable Page Cache for Multiple Solid-State Drives
No full textThis paper presents a scalable page cache called ScaleCache for improving SSD scalability. Specifically, we first propose a concurrent data structure of page cache based on XArray (ccXArray) to enable access and update the page cache concurrently. Second, we introduce a direct page flush (dflush) which directly flushes pages to storage devices in a parallel and opportunistic manner. We implement ScaleCache with two techniques in the Linux kernel and evaluate it on a 64-core machine with eight NVMe SSDs. Our evaluations show that ScaleCache improves the performance of Linux file systems by up to 6.81× and 4.50× compared with the existing scheme and scalable scheme for multiple SSDs, respectively. © 2024 ACM
Optimal cell configuration for accessing Zn reversibility in flowless Zn-Br batteries
No full text리튬 이온 전지를 대체할 차세대 에너지 저장장치로 무흐름 아연-브롬 전지 기반의 초 저가 레독스 시스템이 크게 관심을 받고 있다. 그러나, 고 에너지 밀도 구현을 위한 고 용량 전착 특성에 따른 전지 단락의 문제점이 대두된다. 본 연 구진은 전지 구조의 설계를 통한 전지의 성능 차이를 규명하고, 정확한 아연 가역성 평가를 위한 최적의 설계를 제시한다.
Flowless Zn-Br batteries (FL-ZBB) are emerging as a promising next-generation energy storage system to potentially replace lithium-ion batteries due to their ultra-low-cost material properties. However, challenges such as cell failure caused by high-capacity Zn deposition characteristics pose significant obstacles to achieving high energy density. This study examines the performance variations arising from different cell structure designs and proposes an optimal cell design for accurately assessing Zn reversibility.FALSEkcikci_cand
Roll-to-roll fabrication and characterization of ultra-thin ceramic-coated separator for high-energy-density lithium-ion batteries
No full textCeramic-coated separators (CCSs) used in lithium-ion batteries (LIBs) play an important role in ensuring the safety of LIBs. However, conventional CCSs fabricated by coating the slurry consisting of ceramic particles and binder have limitations due to the micrometer-thick ceramic coating layer (CCL), compromising the energy density and power capabilities of LIBs. To address this relationship, ultra-thin CCSs fabricated by the sputtering process have attracted attention due to their high thermal stability even with nanometer-thick CCL. Nevertheless, batch-type sputtering machines cannot provide roll-based CCSs for commercial LIBs. Herein, we demonstrate the continuous fabrication of ultra-thin CCS rolls with a pilot-scale direct current (DC) roll-to-roll sputtering process. The ionic conductance and thermal stability are compared with the slurry-based CCS by controlling the coating thickness through the line speed. In addition, the electrochemical performance of the pouch cell (LiNi0.6Co0.2Mn0.2O2/graphite, 550 mAh) is evaluated to confirm the applicability of the roll-to-roll sputtered ultra-thin, binder-free CCS (R2R-UB-CCS) to LIBs. Owing to the nanometer-thick CCL, the pouch cell with the R2R-UB-CCS shows higher power capabilities than the bare polyethylene (PE) separator and slurry-based CCS. Thus, the roll-to-roll sputtering process for large-scale, high-speed mass production of ultra-thin CCS has the potential for high safety and high-energy-density LIBs. © 2024 Elsevier B.V.FALSEsciescopu
Autonomous Traffic and Communication Integrated Simulator for V2X Performance Evaluation
No full textThis paper proposes an integrated simulator combining WiLabV2Xsim, a MATLAB-based open-source simulator, and Virtual Test Drive (VTD), a traffic generation and vehicle dynamics simulator developed by HEXAGON, to evaluate Vehicle to Everything (V2X) communication performance in real road and driving environments. WiLabV2Xsim is a system-level simulator that implements Cellular-V2X and New Radio-V2X communication protocol stacks. Through VTD, it is possible to implement usecases standardized in 3GPP Technical Report (TR) 22.886 or evaluate V2X communication performance in real environments using virtual environments based on the Association for Standardization of Automation and Measuring Systems (ASAM) Open Scenario. Future research plans include evaluating various scenarios based on the 5G Automotive Association (5GAA) TR documents through the integrated simulator and conducting studies on wireless resource allocation and decentralized congestion control to enhance V2X communication performance. © 2024 IEEE
An Optical Interferometer-based Force Sensor System for Enhancing Precision in Epidural Injection Procedure
No full textIn minimally invasive pain management procedures, precise needle positioning is paramount for effective treatment and patient safety. Traditional techniques like the loss-of-resistance (LOR) method may be insufficient, especially in patients with narrowed epidural spaces. The use of imaging tools such as C-arms carries risks due to radiation exposure for medical professionals. A new system for detecting the epidural space based on optical interferometry is proposed to tackle this issue. Prior research has focused on force measurement systems to identify tissue puncture or rupture. Although mechanical sensors have been utilized, they add bulk and complexity to systems. Optical sensors like Fiber Bragg grating (FBG) and Fabry-Pérot interferometer (FPI) offer stable, high-resolution measurements suitable for complex biological tissues. This study aims to develop a sensor and needle system for epidural injections, incorporating quantitative metrics for validation. An optical interferometer-based force measurement sensor was integrated into a commercial epidural needle, and calibration was performed to establish a correlation between system output and actual force. The system employs a graphical user interface (GUI) to identify puncture points based on abrupt force decreases. A user study involving interventionalists assessed the system's performance by measuring invasive depth and success rates. The user study demonstrated that the proposed sensorized system could detect the puncture with an average success rate of 72.63 %. This study represents a significant advancement toward safer and more precise epidural procedures, addressing critical clinical considerations for practical applications. © 2024 IEEE
Resting state of human brain measured by fMRI experiment is governed more dominantly by essential mode as a global signal rather than default mode network
Get PDFResting-state of the human brain has been described by a combination of various basis modes including the default mode network (DMN) identified by fMRI BOLD signals in human brains. Whether DMN is the most dominant representation of the resting-state has been under question. Here, we investigated the unexplored yet fundamental nature of the resting-state. In the absence of global signal regression for the analysis of brain-wide spatial activity pattern, the fMRI BOLD spatiotemporal signals during the rest were completely decomposed into time-invariant spatial-expression basis modes (SEBMs) and their time-evolution basis modes (TEBMs). Contrary to our conventional concept above, similarity clustering analysis of the SEBMs from 166 human brains revealed that the most dominant SEBM cluster is an asymmetric mode where the distribution of the sign of the components is skewed in one direction, for which we call essential mode (EM), whereas the second dominant SEBM cluster resembles the spatial pattern of DMN. Having removed the strong 1/f noise in the power spectrum of TEBMs, the genuine oscillatory behavior embedded in TEBMs of EM and DMN-like mode was uncovered around the low-frequency range below 0.2 Hz. © 2024TRUEsciescopu
Target-specified reference-based deep learning network for joint image deblurring and resolution enhancement in surgical zoom lens camera calibration
No full textBackground and objective: For the augmented reality of surgical navigation, which overlays a 3D model of the surgical target on an image, accurate camera calibration is imperative. However, when the checkerboard images for calibration are captured using a surgical microscope having high magnification, blur owing to the narrow depth of focus and blocking artifacts caused by limited resolution around the fine edges occur. These artifacts strongly affect the localization of corner points of the checkerboard in these images, resulting in inaccurate calibration, which leads to a large displacement in augmented reality. To solve this problem, in this study, we proposed a novel target-specific deep learning network that simultaneously enhances both the blur and spatial resolution of an image for surgical zoom lens camera calibration. Methods: As a scheme of an end-to-end convolutional deep neural network, the proposed network is specifically intended for the checkerboard image enhancement used in camera calibration. Through the symmetric architecture of the network, which consists of encoding and decoding layers, the distinctive spatial features of the encoding layers are transferred and merged with the output of the decoding layers. Additionally, by integrating a multi-frame framework including subpixel motion estimation and ideal reference image with the symmetric architecture, joint image deblurring and enhanced resolution were efficiently achieved. Results: From experimental comparisons, we verified the capability of the proposed method to improve the subjective and objective performances of surgical microscope calibration. Furthermore, we confirmed that the augmented reality overlap ratio, which quantitatively indicates augmented reality accuracy, from calibration with the enhanced image of the proposed method is higher than that of the previous methods. Conclusions: These findings suggest that the proposed network provides sharp high-resolution images from blurry low-resolution inputs. Furthermore, we demonstrate superior performance in camera calibration by using surgical microscopic images, thus showing its potential applications in the field of practical surgical navigation. © 2024FALSEsciescopu
Keratin 17 tail domain has a role in keratin 6/17 filament formation
No full textKeratins are the largest subgroup of intermediate filament (IF) proteins, which form 10-nm filaments with type I/II heterodimers in epithelial cells. The keratin 6 (K6) and keratin 17 (K17) pairs (K6/K17) are stress-induced keratins specifically expressed when wounds occur on the skin and play a key role in driving wound healing. To perform these functions, keratins require the assembly and bundling of filaments. It has been known that the rod domain plays a major role in forming this filament structure, and tail domains are not required for filament formation. For example, a tail deletion mutant of the K14, the basal layer-specific keratin, can form 10-nm filaments indistinguishable from wild-type keratins. In this study, however, we suggest that the K17 tail domain (K17T) plays a significant role in filament structure formation in the case of K6/K17. In this report, we used recombinant human proteins, including wildtype K6, wildtype K17, K17 rod domain (K17R), and tail-less K17 (K17ΔT), which were purified and reconstituted as described in previous works of literature. We conducted in vitro filament assembly studies on K6/K17, K6/K17R, and K6/K17ΔT pairs. To verify the abilities for filament structure formation among the groups, a high-speed sedimentation assay with Air-driven ultracentrifuge (Airfuge) was used. Using Transmission Electron Microscope (TEM), we directly observed the filament formation patterns of each group. Also, to elucidate the mechanism of how K17T influences K6/17 filament formation, we conducted a binding assay for K17T using a cross-linker, disuccinimidyl suberate (DSS). Furthermore, for structural analysis of K17T, NMR experiments with 13C- & 15N-labeled K17T were conducted. In the high-speed sedimentation assay using Airfuge, the K6/K17 group showed that most of the proteins were retrieved from the pellet. However, the K6/K17ΔT group displayed only about half of the proteins in the pellet. The K6/K17R group also revealed that most of the proteins were found in the supernatant. Using TEM, we observed that the K6/K17 group exhibited an elongated filament structure, while the K6/K17ΔT group displayed truncated filaments with the presence of spherical particles. In the case of the K6/K17R group, it did not exhibit any filamentous structures, only spherical particles. In the binding assay for K17T using the cross-linker DSS, K17T formed self-dimerization. In the structural analysis using NMR experiments, we discovered that K17T exhibited a high level of disorder. In the High-speed sedimentation assay, the majority of K6/K17 proteins were localized in the pellet, whereas the K6/K17ΔT group exhibited about half of the proteins in the pellet. This suggests that although the keratin 17 tail is not indispensable, it significantly influences keratin filament formation, as also confirmed by TEM imaging. TEM observations further confirmed that the K6/K17R pair failed to generate filaments and only formed particles, highlighting the insufficiency of the K17 rod alone for K6/K17 filament assembly. Additionally, with the presence of shorter filaments and spherical particles in the K6/K17ΔT group, we can infer the contributory roles of the K17T in both filament formation and stabilization. As the mechanism of how the K17T stabilizes the K6/K17 filament structures, we suggest interactions between the K17T, as confirmed by the binding assay with DSS. We also conducted a structural analysis for the K17T, and we have confirmed that K17T exhibits intrinsically disordered protein (IDP) characteristics
Effects of Computer Mouse Lift-off Distance Settings in Mouse Lifting Action
No full textThis study investigates the effect of Lift-off Distance (LoD) on a computer mouse, which refers to the height at which a mouse sensor stops tracking when lifted off the surface. Although a low LoD is generally preferred to avoid unintentional cursor movement in mouse lifting (=clutching), especially in first-person shooter games, it may reduce tracking stability. We conducted a psychophysical experiment to measure the perceptible differences between LoD levels and quantitatively measured the unintentional cursor movement error and tracking stability at four levels of LoD while users performed mouse lifting. The results showed a trade-off between movement error and tracking stability at varying levels of LoD. Our findings offer valuable information on optimal LoD settings, which could serve as a guide for choosing a proper mouse device for enthusiastic gamers. © 2024 Owner/Author
Predicting Obstructive Sleep Apnea Based on Computed Tomography Scans Using Deep Learning Models
No full textRationale: The incidence of clinically undiagnosed obstructive sleep apnea (OSA) is high among the general population because of limited access to polysomnography. Computed tomography (CT) of craniofacial regions obtained for other purposes can be beneficial in predicting OSA and its severity. Objectives: To predict OSA and its severity based on paranasal CT using a three-dimensional deep learning algorithm. Methods: One internal dataset (N = 798) and two external datasets (N = 135 and N = 85) were used in this study. In the internal dataset, 92 normal participants and 159 with mild, 201 with moderate, and 346 with severe OSA were enrolled to derive the deep learning model. A multimodal deep learning model was elicited from the connection between a three-dimensional convolutional neural network-based part treating unstructured data (CT images) and a multilayer perceptron-based part treating structured data (age, sex, and body mass index) to predict OSA and its severity. Measurements and Main Results: In a four-class classification for predicting the severity of OSA, the AirwayNet-MM-H model (multimodal model with airway-highlighting preprocessing algorithm) showed an average accuracy of 87.6% (95% confidence interval [CI], 86.8-88.6%) in the internal dataset and 84.0% (95% CI, 83.0-85.1%) and 86.3% (95% CI, 85.3-87.3%) in the two external datasets, respectively. In the two-class classification for predicting significant OSA (moderate to severe OSA), the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, and F1 score were 0.910 (95% CI, 0.899-0.922), 91.0% (95% CI, 90.1-91.9%), 89.9% (95% CI, 88.8-90.9%), 93.5% (95% CI, 92.7-94.3%), and 93.2% (95% CI, 92.5-93.9%), respectively, in the internal dataset. Furthermore, the diagnostic performance of the Airway Net-MM-H model outperformed that of the other six state-of-the-art deep learning models in terms of accuracy for both four- and two-class classifications and area under the receiver operating characteristic curve for two-class classification (P, 0.001). Conclusions: A novel deep learning model, including a multimodal deep learning model and an airway-highlighting preprocessing algorithm from CT images obtained for other purposes, can provide significantly precise outcomes for OSA diagnosis. Copyright © 2024 by the American Thoracic Society.FALSEsciescopu