Dohyeong Kim

Precise control of elemental configurations within multimetallic nanoparticles (NPs) could enable access to functional nanomaterials with significant performance benefits. This can be achieved down to the atomic level by the disorder-to-order transformation of individual NPs. Here, by systematically controlling the ordering degree, we show that the atomic ordering transformation, applied to AuCu NPs, activates them to perform as selective electrocatalysts for CO 2 reduction. In contrast to the d

The apparent incongruity between the increasing consumption of fuels and chemicals and the finite amount of resources has led us to seek means to maintain the sustainability of our society. Artificial photosynthesis, which utilizes sunlight to create high-value chemicals from abundant resources, is considered as the most promising and viable method. This Minireview describes the progress and challenges in the field of artificial photosynthesis in terms of its key components: developments in phot

Significance Electrochemical conversion of CO 2 to carbon-based products, which can be used directly as fuels or indirectly as fuel precursors, is suggested as one of the promising solutions for sustainability. Not only does this process allow using renewables such as solar electricity as energy input, but CO 2 emitted from the consumption process can be recycled back into fuels. The success of this technology depends on the value added to the product that forms from CO 2 , and therefore it is i

Content-centric networking (CCN) is designed for efficient dissemination of information. Several architectures are proposed for CCN recently, but mobility issues are not considered sufficiently. We classify traffic types of CCN into real-time and non real-time. We examine mobility problems for each type, and suggest the possible hand-off schemes over CCN. Then, we analyze the delay performance in terms of simulation study. We believe that the proposed schemes can be merged as a part of the CCN e

Abstract Within the breath lie numerous health indicators, encompassing respiratory patterns and biomarkers extending beyond respiratory conditions to cardiovascular health. Recently, the emergence of the SARS-CoV-2 pandemic has not only underscored the necessity of on-the-spot breath analysis but has also normalized the use of masks in everyday life. Simultaneously, the rapid evolution of wearable technology has given rise to innovative healthcare monitoring tools, with a specific emphasis on w

Abstract Angesichts der Unvereinbarkeit eines zunehmenden Verbrauchs von Kraftstoffen und chemischen Produkten und einer nur endlichen Menge an Ressourcen versuchen wir Möglichkeiten zu finden, wie wir unsere Gesellschaft auf Dauer nachhaltig gestalten können. Die künstliche Photosynthese nutzt das Sonnenlicht, um reichlich vorhandene Ressourcen in hochwertige Chemikalien umzuwandeln. Deshalb gilt sie als die aussichtsreichste Methode. Hier werden Entwicklungen und neueste Fortschritte sowie noc

Abstract This review article explores the transformative advancements in wearable biosignal sensors powered by machine learning, focusing on four notable biosignals: electrocardiogram (ECG), electromyogram (EMG), electroencephalogram (EEG), and photoplethysmogram (PPG). The integration of machine learning with these biosignals has led to remarkable breakthroughs in various medical monitoring and human–machine interface applications. For ECG, machine learning enables automated heartbeat classific

In Named-Data Networking (NDN), content is cached in network nodes and served for future requests. This property of NDN allows attackers to inject poisoned content into the network and isolate users from valid content sources. Since a digital signature is embedded in every piece of content in NDN architecture, poisoned content is discarded if routers perform signature verification; however, if every content is verified by every router, it would be overly expensive to do. In our preliminary work,

In Named Data Networking, contents are retrieved from network caches as well as the content server by their name. This aspect arises severe security concerns on content integrity. Especially, if poisoned contents lie in the network cache, called content store(CS), interests would be served by the poisoned content rather than they propagate toward the content server. Consequently, users whose interests pass through the contaminated CS cannot access the valid content. In order to resolve the probl

In this paper, we propose Squeezed Convolutional Variational AutoEncoder (SCVAE) for anomaly detection in time series data for Edge Computing in Industrial Internet of Things (IIoT). The proposed model is applied to labeled time series data from UCI datasets for exact performance evaluation, and applied to real world data for indirect model performance comparison. In addition, by comparing the models before and after applying Fire Modules from SqueezeNet, we show that model size and inference ti

For intelligent service robots, it is essential to recognize users in order to provide appropriate services to a correctly authenticated user. However, in robot environments in which users freely move around the robot, it is difficult to force users to cooperate for authentication as in traditional biometric security systems. This paper introduces a user authentication system that is designed to recognize users who are unconscious of a robot or of cameras. In the proposed system, biometrics and

This paper suggests a method of classifying Korean pop (K-pop) dances based on human skeletal motion data obtained from a Kinect sensor in a motion-capture studio environment. In order to accomplish this, we construct a K-pop dance database with a total of 800 dance-movement data points including 200 dance types produced by four professional dancers, from skeletal joint data obtained by a Kinect sensor. Our classification of movements consists of three main steps. First, we obtain six core angle