A5065910890- Advanced Battery Materials and Technologies
- Digital Transformation in Industry
- Advancements in Battery Materials
- Advanced Battery Technologies Research
- Software-Defined Networks and 5G
- IoT and Edge/Fog Computing
- Stock Market Forecasting Methods
- Time Series Analysis and Forecasting
- Cloud Computing and Resource Management
- Software System Performance and Reliability
- Advanced Data Processing Techniques
- Human Pose and Action Recognition
- Cloud Data Security Solutions
- Smart Grid Energy Management
- Hand Gesture Recognition Systems
- Chaos-based Image/Signal Encryption
- CO2 Reduction Techniques and Catalysts
- Advanced Text Analysis Techniques
- Innovation in Digital Healthcare Systems
- Smart Parking Systems Research
- Ammonia Synthesis and Nitrogen Reduction
- Advanced Photocatalysis Techniques
- Human Mobility and Location-Based Analysis
- Energy Load and Power Forecasting
- Robotics and Automated Systems
Chonnam National University
2023-2024
Korea Institute of Energy Research
2022-2023
This work presents that Cu with atomic-scale spacings ( d s ) efficiently catalyses the electrochemical co-reduction of CO 2 and NO 3 − to urea. Specifically, near 6 Å (6 Å-Cu) produces urea a high yield rate partial current density.
Herein, we present a gel polymer electrolyte (GPE) improving nonflammability of lithium-ion batteries (LIBs) by blocking radical-initiated chain reactions which cause thermal runaway and finally fire issues. The that makes up the nonflammable GPE was (1) soluble in carbonate electrolytes, (2) cross-linkable presence popularly used lithium salt such as LiPF6, (3) gelated only with 2 wt % (4) radical-scavenging its functional side chains. Electrolytes having were thermally within battery cells...
With the advancement of Internet Things (IoT) and 5G technology, new network services such as Virtual Reality/Augmented Reality (VR/AR) are emerging, leading to expansion scale increasing traffic. As a solution complexity operation maintenance, Digital Twin Network (DTN) technology has been introduced. In order provide visibility into various aspects for management, observation analysis data necessary. Therefore, this paper presents types available in digital twin lifecycle process...
Graph Neural Networks (GNNs) have emerged as a powerful framework for analyzing and extracting information from complex network data. In the realm of Digital Twin (DTN), where physical entities are mirrored in virtual environment, GNNs offer transformative approach by leveraging inherent structure relationships within digital twins. enable enhanced data representation predictive modeling. DTNs encompass several core elements that naturally conform to graph-like structure, including aspects...
The proliferation of immersive services, including virtual reality/augmented reality, holographic content, and the metaverse, has led to an increase in complexity communication networks, consequently, network management. Recently, digital twin technology, which applies technology field been predicted be effective means managing complex modern networks. In this paper, a data pipeline architecture is proposed that demonstrates integrated structure for flow within modeling from perspective....
Digital Twins, which are virtual representations of physical systems mirroring their behavior, enable real-time monitoring, analysis, and optimization. Understanding identifying the temporal dependencies included in multivariate time series data that characterize behavior system crucial for improving effectiveness Twins. Long Short-Term Memory (LSTM) networks have been used to represent complex identify long-term links Industrial Internet Things (IIoT). This paper proposed a Twin dependency...
Despite constituting only a small fraction of the mass an electrode, binder is one key components ensuring long-term durability lithium-ion batteries (LIBs). The need for higher energy density and superior power characteristics necessitates enhancement adhesion properties to reduce contents electrodes. In this study, we synthesized core-shell structured poly(acrylonitrile-co‑butyl acrylate) that exhibits strength, lower kinetic resistance, electrode cell diffusion coefficients than those...
Electricity consumption has been increasing steadily owing to technological developments since the Industrial Revolution. Technologies that can predict power usage and management for improved efficiency are thus emerging. Detailed energy requires precise forecasting. Deep learning technologies have widely used recently achieve high performance. Many deep focused on accuracy, but they do not involve detailed time-based prediction research. In addition, models should consider computing power,...
A new network named the "Digital Twin Network" (DTN) uses Twin" (DT) technology to produce virtual twins of real things. The load and size continue grow as a result development 5G, Internet Things, cloud computing well advent services. As result, operation maintenance are becoming more difficult. digital twin connects worlds, exchanging data in both directions revealing information about progression process. framework Industrial processing, is taken into consideration this article key...
With the emergence and proliferation of new forms large-scale services such as smart homes, virtual reality/augmented reality, increasingly complex networks are raising concerns about significant operational costs. As a result, need for network management automation is emphasized, Digital Twin Networks (DTN) technology expected to become foundation autonomous networks. DTN has advantage being able operate system based on real-time collected data in closed-loop system, currently it mainly...
The Metaverse is anticipated to provide an immersive experience a large group of simultaneous users. Optimal computing resource allocation critical meet the massive users' demand. However, existing studies pay little attention tasks decoupling in joint dynamic problems. This paper considers metaverse decoupling, offloading and problem for deployment. We model as mixed non-linear programming (MNLP) minimize network energy consumption task completion delay. employ fuzzy c-means (FCM)...
Super resolution is a technology that converts low-resolution images into high-resolution images, and super technologies using deep learning have recently been studied. The SRCNN model was first studied as learning-based super-resolution technology, the performs early up-sampling interpolation to perform super-resolution. In this paper, best performance investigated through comparison of method. Through this, it found bicubic method derived good from up 35% at least 17% compared other methods.
With the recent increase in demand for personal mobility and emergence of shared e-scooter companies, accidents related to e-scooters have also increased rapidly. In order reduce these accidents, it is necessary detect illegal activity such as riders not wearing a helmet or multiple people riding same scooter. It estimated that there are far more cases on scooters been detected, even though put place driving. this paper, we proposed detection model media resolve problem. For real-time...
With the emergence of a flexible mix private and public clouds based on business requirements, need for system that supports application deployment to variety cloud environments has emerged. In particular, it is necessary secure security data in applications federated learning monitor resource usage cloud. This paper seeks ways manage according various hyperparameters when conducting hybrid environment. Docker-based environment, we present an improved method using efficient resources while...
Information security or data transmission efficiency and reliability are unanimously considered as some of the most important things that required whenever communication between parties is to be established. Inefficient improper management voluminous during increases cost, delay in transmission, risk confidentiality privacy transmitted data. Thus, this paper proposes an improved cryptosystem ensure channel. The work adopted two cryptographic methods (ElGamal cryptosystem) for asymmetric key...
In multivariate time series data, the analysis of temporal dependencies is crucial for comprehending complex relationships, forecasting trends, and streamlining diverse applications. this study, we proposed a method improving data using dependence modeling. Our approach based on utilizing cutting-edge machine learning methods, particularly Long Short-Term Memory (LSTM) networks, that are well known their capacity to record complicated correlations. We want reveal hidden patterns within by...
Solid electrolytes have been regarded as promising candidates replacing organic liquid electrolyte due to much enhanced safety, which is able use lithium metal an anode material for high energy system. Among several solid electroytes, garnet-type has wide electrochemical window well chemical stability and ionic conductivity at room temperature. However, from assembled full cell’s point of view, interfacial resistance between electrode a huge obstacle should be overcome. Herein, we...
Despite constituting only a small fraction of the mass an electrode, binder is one key components ensuring long-term durability lithium-ion batteries (LIBs). The need for higher energy density and superior power characteristics necessitates enhancement adhesion properties to reduce contents electrodes. In this study, we synthesized core-shell structured poly(acrylonitrile- co -butyl acrylate) that exhibits strength, lower kinetic resistance, electrode cell diffusion coefficients than those...
As the number of clients who use application increases, various developers and providers are paying more attention to determining internal structure service in order provide a suitable service. In particular, when AI is built-in within service, there clear limit operating on single server embed complicated computation automated model building execution. this paper, we investigated cases using hybrid cloud-based virtualization platform effectively AI-based services huge users. Most have...
Recently, it is trying to realize zero carbon through energy management based on power consumption prediction. To this end, research being conducted predict using a deep learning model. Data pre-processing essential for model learning. However, there problem that such optimization works according empirical design. Genetic algorithms are suitable these tasks. We propose data method genetic algorithms. learned the RNN optimized and derive up 83% higher performance than heuristic method-based
Solid electrolytes have been regarded as promising candidates replacing organic liquid electrolyte due to much enhanced safety, which is able use lithium metal an anode material for high energy system. Among several solid electroytes, garnet-type has wide electrochemical window well chemical stability and ionic conductivity at room temperature. However, from assembled full cell’s point of view, interfacial resistance between electrode a huge obstacle should be overcome. Herein, we...