A5043980889- Advancements in Battery Materials
- Advanced Battery Materials and Technologies
- Advanced Battery Technologies Research
- Bioinformatics and Genomic Networks
- Gene expression and cancer classification
- Advanced battery technologies research
- Machine Learning in Bioinformatics
- Mental Health Research Topics
- Mental Health via Writing
- Extraction and Separation Processes
- Human Pose and Action Recognition
- Psychological Well-being and Life Satisfaction
- Metabolomics and Mass Spectrometry Studies
- Retinal Diseases and Treatments
- Education and Learning Interventions
- Topic Modeling
- Digital Mental Health Interventions
- Emotion and Mood Recognition
- Technology and Data Analysis
- Retinal Imaging and Analysis
- Natural Language Processing Techniques
- Multimodal Machine Learning Applications
- Cognitive Abilities and Testing
- Music and Audio Processing
- Video Surveillance and Tracking Methods
Electronics and Telecommunications Research Institute
2023-2024
Gwangju Institute of Science and Technology
2021-2023
Seoul National University
2017-2021
Institute for Basic Science
2021
Seoul Institute
2018
Government of the Republic of Korea
2018
Korea Electrotechnology Research Institute
2011
Recent discovery of high-concentration electrolyte systems has opened a new avenue toward the high-voltage, safe, and low-cost aqueous rechargeable batteries. However, need for generally high-cost organic solutes in become another major obstacle. Herein, we revisited all commonly used system discovered that use NaClO4 solute effectively results wide electrochemical stability window by suppressing water decomposition induces stable solid-electrolyte interphase (SEI) layer formation without...
We report that the self-discharge of lithium-ion batteries can be abnormally accelerated when thermal ‘history’ is memorized as form an internal ‘parasitic’ lithium source.
Metallic lithium (Li) is a promising anode candidate for high-energy-density rechargeable batteries because of its low redox potential and high theoretical capacity. However, practical application not imminent issues related to the dendritic growth Li metal with repeated battery operation, which presents serious safety concern. Herein, various aspects electrochemical deposition stripping are investigated consideration reaction rate/current density, electrode morphology, solid electrolyte...
Abstract The production of rechargeable batteries is rapidly expanding, and there are going to be new challenges in the near future about how potential environmental impact caused by disposal large volume used can minimized. Herein, a novel strategy proposed address these concerns applying biodegradable device technology. An eco‐friendly sodium‐ion secondary battery (SIB) developed through extensive material screening followed synthesis electrodes their seamless assembly with an...
Abstract Motivation Accurate diagnostic classification and biological interpretation are important in biology medicine, which data-rich sciences. Thus, integration of different data types is necessary for the high predictive accuracy clinical phenotypes, more comprehensive analyses predicting prognosis complex diseases required. Results Here, we propose a novel multi-task attention learning algorithm multi-omics data, termed MOMA, captures processes performance interpretability. MOMA...
Abstract To develop an artificial intelligence (AI) model that predicts anti-vascular endothelial growth factor (VEGF) agent-specific anatomical treatment outcomes in neovascular age-related macular degeneration (AMD), thereby assisting clinicians selecting the most suitable anti-VEGF agent for each patient. This retrospective study included patients diagnosed with AMD who received three loading injections of either ranibizumab or aflibercept. Training was performed using optical coherence...
Abstract Shedding new light on conventional batteries sometimes inspires a chemistry adoptable for rechargeable batteries. Recently, the primary lithium-sulfur dioxide battery, which offers high energy density and long shelf-life, is successfully renewed as promising system exhibiting small polarization good reversibility. Here, we demonstrate first time that reversible operation of battery also possible by exploiting carbonate-based electrolytes. Theoretical experimental studies reveal...
Increasing demands for advanced lithium batteries with higher energy density have resurrected the use of metal as an anode, whose practical implementation has still been restricted, because its intrinsic problems originating from high reactivity elemental metal. Herein, we explore a facile strategy doping gas phase into electrolyte to stabilize and suppress selective growth through formation stable homogeneous solid interphase (SEI) layer. We find that sulfur dioxide additive doped in...
Integration of multi-omics data provides opportunities for revealing biological mechanisms related to certain phenotypes. We propose a novel method integration called supervised deep generalized canonical correlation analysis (SDGCCA) modeling structures between nonlinear manifolds that aims at improving the classification phenotypes and biomarkers SDGCCA addresses limitations other (CCA)-based models (such as CCA, CCA) by considering complex/nonlinear cross-data correlations multiple (≥2)...
High dimensional multi-omics data integration can enhance our understanding of the complex biological interactions in human diseases. However, most studies involving unsupervised focus on linear methods. In this study, we propose a joint deep semi-non-negative matrix factorization (JDSNMF) model, which uses hierarchical non-linear feature extraction approach that capture shared latent features from data. The extracted obtained JDSNMF enabled variety downstream tasks, including prediction...
Increasing demands for performance beyond the limit of current lithium ion batteries higher energy densities have rejuvenated research using metal as an anode.
Multimodal learning often outperforms its unimodal counterparts by exploiting contributions and cross-modal interactions. However, focusing only on integrating multimodal features into a unified comprehensive representation overlooks the characteristics. In real data, of modalities can vary from instance to instance, they reinforce or conflict with each other. this study, we introduce novel \text{MultiModal} loss paradigm for learning, which subgroups instances according their contributions....
Monitoring human behavior through wearable devices has potential in psychiatry. Among them, actigraphy data been used to classify depression and detect depressive symptoms. We aim collect a larger number of measure classification performance. This study evaluates the performance classifying symptoms solely on using both public (n=1549) collected (n=3145) datasets. found that there are challenges from data.
Recently, there has been a significant amount of research conducted on classifying depression by extracting features from voice data. In this study, we enhanced the dataset segmenting data into 4-second intervals, followed extraction using MFCC (Mel Frequency Cepstral Coefficients) and Mel-spectrogram. After these features, compared performance depending models found that with XGBoost model yielded best performance. Looking forward, aim to enhance model's utilizing multimodal data, including...
This study examined the properties of 1 wt.% vinylene carbonate, vinyl ethylene and diphenyloctyl phosphate additive electrolytes as a promising way beneficially improving surface cell resistance Li-ion batteries. The were dominant both in formation internal resistance. In particular, electrochemical impedance spectroscopy, Fourier transform infrared spectroscopy scanning electron microscopy confirmed that is an excellent to electrolyte batteries due improved co-intercalation solvent molecules.
Elemental lithium metal has received a great attention as an ideal high-energy-density negative electrode material for batteries, owing to the highest theoretical specific capacity (3860 mAh g -1 ) and lowest electrochemical potential (-3.040V vs. standard hydrogen electrode). While these latent merits have been significantly plagued by catastrophic safety issues of anodes, which are tied chronic problems such (1) dendritic growth (2) high reactivity metallic in electrolyte, recent intense...
Metallic lithium (Li) is a promising anode candidate for high-energy-density rechargeable batteries because of its low redox potential and high theoretical capacity. However, practical application not imminent issues related to the dendritic growth Li metal with repeated battery operation, which presents serious safety concern. Herein, various aspects electrochemical deposition stripping are investigated consideration reaction rate/current density, electrode morphology, solid electrolyte...
Integration of multi-omics data provides opportunities for revealing biological mechanisms related to certain phenotypes. We propose a novel method integration called supervised deep generalized canonical correlation analysis (SDGCCA) modeling structures between nonlinear manifolds, aiming improving classification phenotypes and biomarkers SDGCCA addresses the limitations other (CCA)-based models (e.g., CCA, CCA) by considering complex/nonlinear cross-data correlations discriminating...