Daegu Gyeongbuk Institute of Science and Technology
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Enriched supercapacitive performance of electrochemically tailored β-Co(OH)2/CoOOH nanodiscs from sacrificial Co3(PO4)2·4H2O microbelts
No full textElectrochemically induced structural reconstruction process of metal precursors into their corresponding metal hydroxides/(oxy)hydroxides is developed as an ingenious strategy towards preparing efficient electrode material. However, the influence of electrochemical activation on the charge storage ability of electrode material is barely explored. Herein, we have utilized a synthetic strategy (solvothermal reaction in water:ethanol mixture) to prepare Co3(PO4)2·4H2O microbelts and subsequently applied the electrochemical bulk reconstruction process in 1 M KOH electrolyte. A thorough physical and electrochemical characterization unveil that the electrode material is converted into redox active site rich β-Co(OH)2/CoOOH nanodiscs via etching of lattice anionic PO43− moieties. The material possesses a specific capacitance of 386 F g−1at a current density of 0.25 A g−1. Additionally, a hybrid device constructed with β-Co(OH)2/CoOOH nanodiscs and activated charcoal (AC) as positive and negative electrodes has achieved a high energy density of 13.33 Wh Kg−1at a power density of 400 W kg−1. Further, the device displayed a good capacitance retention of 93 % (columbic efficiency ~98 %) up to 10,000 cycles. The improved charge storage of reconstructed material with abundant redox active sites could be attributed to the facile diffusion of electrolyte ions into the bulk of the material. © 2024 Elsevier LtdFALSEsciescopu
Highly Monodisperse Perovskite Colloidal Quantum dots for High-Efficiency Photovoltaics
No full text본 발명은 양자점의 에너지 손실을 유발하는 밴드 테일(band tail)의 확장을 효과적으로 억제시킬 수 있는 양자점에 관한 것으로, 본 발명에 따른 무기계 페로브스카이트 양자점은 AMX3으로 표기되는 페로브스카이트 화합물로서, 상기 A는 1가 금속이온이고, 상기 M은 2가 금속이온이며, 상기 X는 할로겐 음이온이며, 단분산도(monodispersity)를 가지는 것을 특징으로 한다
SSD DEVICE AND OPERATING METHOD OF THE SAME USING FTL BASED ON LSM-TREE AND APPROXIMATE INDEXING
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Multi-proteomic analyses of 5xFAD mice reveal new molecular signatures of early-stage Alzheimer's disease
Get PDFAn early diagnosis of Alzheimer's disease is crucial as treatment efficacy is limited to the early stages. However, the current diagnostic methods are limited to mid or later stages of disease development owing to the limitations of clinical examinations and amyloid plaque imaging. Therefore, this study aimed to identify molecular signatures including blood plasma extracellular vesicle biomarker proteins associated with Alzheimer's disease to aid early-stage diagnosis. The hippocampus, cortex, and blood plasma extracellular vesicles of 3- and 6-month-old 5xFAD mice were analyzed using quantitative proteomics. Subsequent bioinformatics and biochemical analyses were performed to compare the molecular signatures between wild type and 5xFAD mice across different brain regions and age groups to elucidate disease pathology. There was a unique signature of significantly altered proteins in the hippocampal and cortical proteomes of 3- and 6-month-old mice. The plasma extracellular vesicle proteomes exhibited distinct informatic features compared with the other proteomes. Furthermore, the regulation of several canonical pathways (including phosphatidylinositol 3-kinase/protein kinase B signaling) differed between the hippocampus and cortex. Twelve potential biomarkers for the detection of early-stage Alzheimer's disease were identified and validated using plasma extracellular vesicles from stage-divided patients. Finally, integrin α-IIb, creatine kinase M-type, filamin C, glutamine γ-glutamyltransferase 2, and lysosomal α-mannosidase were selected as distinguishing biomarkers for healthy individuals and early-stage Alzheimer's disease patients using machine learning modeling with approximately 79% accuracy. Our study identified novel early-stage molecular signatures associated with the progression of Alzheimer's disease, thereby providing novel insights into its pathogenesis. © 2024 The Authors. Aging Cell published by Anatomical Society and John Wiley & Sons Ltd.TRUEsciescopu
The Value of In-Line Metrology for Advanced Process Control: AM: Advanced Metrology
No full textIn-line metrology provides critical information for feedback and feedforward process control. In high-volume manufacturing, the fundamental question is: how fast, how frequent, and how accurate measurements should be made to satisfy control requirements. This paper develops a framework to study the tradeoff among the sampling rate, the delay, and the quality of measurements and the effect of these canonical factors on the variability of processes. As a consequence of this fundamental tradeoff, the relative value of virtual metrology with respect to real metrology can be quantified in the context of advanced process control. © 2024 IEEE
Enhancing lane detection with a lightweight collaborative late fusion model
Get PDFResearch in autonomous systems is gaining popularity both in academia and industry. These systems offer comfort, new business opportunities such as self-driving taxis, more efficient resource utilization through car-sharing, and most importantly, enhanced road safety. Different forms of Vehicle-to-Everything (V2X) communication have been under development for many years to enhance safety. Advances in wireless technologies have enabled more data transmission with lower latency, creating more possibilities for safer driving. Collaborative perception is a critical technique to address occlusion and sensor failure issues in autonomous driving. To enhance safety and efficiency, recent works have focused on sharing extracted features instead of raw data or final outputs, leading to reduced message sizes compared to raw sensor data. Reducing message size is important to enable collaborative perception to coexist with other V2X applications on bandwidth-limited communication devices. To address this issue and significantly reduce the size of messages sent while maintaining high accuracy, we propose our model: LaCPF (Late Collaborative Perception Fusion), which uses deep learning for late fusion. We demonstrate that we can achieve better results while using only half the message size over other methods. Our late fusion framework is also independent of the local perception model, which is essential, as not all vehicles on the road will employ the same methods. Therefore LaCPF can be scaled more quickly as it is model and sensor-agnostic. © 2024 The AuthorsTRUEsciescopu
Composite comprising Platinum-alkaline earth metal Alloy, Fuel Cell and water electrolyzer comprising the Same and Manufacturing Method Thereof
No full textImpact of Joint Heat and Memory Constraints of Mobile Device in Edge-Assisted On-Device Artificial Intelligence
No full textRecently, consumer demand for artificial intelligence (AI) applications using deep neural network (DNN) model such as large language model (LLM), miXed Reality (XR), and AI assistants has been steadily increasing. Hitherto, on-device AI and offloaded analytics with the help of mobile edge computing (MEC) have been extensively studied to realize AI services on top of mobile devices. However, both technologies suffer from the limited resources of mobile devices, such as thermal resilience, battery capacity, and memory size. To tackle this problem, we first extensively examine the impact of heat and memory constraints of a mobile device when networking and processing resources and multi-dimensional DNN model sizes are dynamically managed for AI applications via motivating measurement. From the experimental results, we conjecture that the threshold-based approach for joint consideration of heat and memory constraints would increase the performance of AI applications in terms of energy, frames per second (FPS), and inference accuracy. Hence, we propose a threshold-based H&M algorithm that jointly adjusts offloading, Dynamic Voltage and Frequency Scaling (DVFS), and DNN model size, aiming to maximize inference accuracy while keeping target FPS with memory and heat constraints in various environments. Finally, we implement the proposed scheme on a mobile device and an MEC server and evaluate its performance and adaptability via extensive experiments. © 2024 Owner/Author
A Ready-to-Use RTL Generator for Systolic Tensor Arrays and Analysis Using Open-Source EDA Tools
No full textFrom simple image classifiers to complex and large language models, generalized matrix multiplication (GEMM) is the fundamental and the most time-consuming operation among all mathematical operations involved in them. To accelerate the computation of matrix multiplication in deep learning, many off-the-shelf neural processors utilize systolic arrays as dedicated hardware for the GEMM operations. Recently, more generalized form of the systolic array, i.e., a systolic tensor array (STA) which includes vectorized MAC units within a single processing unit, has been proposed. However, the optimal selection of STA configuration on a given deep learning model is difficult due to large configuration search space. To help select the optimal STA configuration in many deep learning models, in this work, we present a ready-to-use and open-source RTL generator for various STA configurations. The power consumption and post-layout area of several STAs are analyzed by using open-source EDA tools. © 2024 IEEE