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Optimizing Hemocompatibility of Surfactant-Coated Silver Nanoparticles in Human Erythrocytes
Kwon, TaeWoo,Woo, Hyun Je,Kim, Young Ha,Lee, Hyun Ju,Park, Kang Hyun,Park, Sungkyun,Youn, BuHyun American Scientific Publishers 2012 Journal of Nanoscience and Nanotechnology Vol.12 No.8
<P>Several recent biological science studies have been focused on nanotechnology and nanomaterials due to their potential use in biomedicine. Drug delivery systems are an example of biomedical applications utilizing nanoparticles. Silver nanoparticles (AgNPs) can be used for these drug delivery systems. However, the effects of cytotoxicity caused by AgNPs are not fully understood. Determining the optimal characteristics to facilitate the biocompatibility of AgNPs is an important subject for application. In the present study, human erythrocytes were used as an in vitro model to examine the size, dose, and coating surfactant-dependent cytotoxicity of AgNPs. Our results demonstrated that polyvinylpyrrolidone (PVP) was a more suitable surfactant than polyethylene glycol (PEG) for AgNPs capping. In addition, we determined the appropriate particular size and dosage of AgNPs to reduce human erythrocytes hemolysis. Membrane damages including hemolysis, potassium efflux, protein leakage, and alterations in cell shape and membrane fragility were minimized with 100-nm AgNP particles. This study provides novel insights into AgNPs cytotoxicity and a basis for utilizing AgNPs for diagnostic and therapeutic applications.</P>
Kwon, TaeWoo,Lee, Daepyo,Lee, Jong-Yong,Jung, Kye-Dong The Institute of Internet 2018 International Journal of Internet, Broadcasting an Vol.10 No.3
The majority of existing falling recognition techniques provide service by recognizing only that the falling occurred. However, it is important to recognize not only the occurrence of falling but also the situation before and after the falling, as well as the location of the falling. In this paper, we design and propose the falling notification service system to recognize and provide service. This system uses the acceleration sensor of the smartphone to recognize the occurrence of a falling and the situation before and after the falling. In order to check the location of falling, GPS sensor data is used in the Google Map API to map to the map. Also, a crosswalk map converted into grid-based coordinates based on the longitude and latitude of the crosswalk is stored, and the locations before and after falling are mapped. In order to reduce the connection speed and server overload for real-time data processing, fog computing and cloud computing are designed to be distributed processing.
Design of Cloud-based Context-aware System Based on Falling Type
TaeWoo Kwon,Jong-Yong Lee,Kye-Dong Jung 한국인터넷방송통신학회 2017 International Journal of Internet, Broadcasting an Vol.9 No.4
To understand whether Falling, which is one of the causes of injuries, occurs, various behavior recognition research is proceeding. However, in most research recognize only the fact that Falling has occurred and provide the service. As well as the occurrence of the Falling, the risk varies greatly based on the type of Falling and the situation before and after the Falling. Therefore, when Falling occurs, it is necessary to infer the user's current situation and provide appropriate services. In this paper, we propose to base on Fog Computing and Cloud Computing to design Context-aware System using analysis of behavior data and process sensor data in real-time. This system solved the problem of increase latency and server overload due to large capacity sensor data.
Dermal Toxicity of Flake-Like 관-Alumina Pigments.
Kwon, TaeWoo,Seo, HyunJeong,Jang, Seongwan,Lee, Sang-Geun,Park, Sungkyun,Park, Kang Hyun,Youn, BuHyun American Scientific Publishers 2015 Journal of Nanoscience and Nanotechnology Vol.15 No.2
<P>Aluminum is one of the most widely used nonferrous metals and an important industrial material, especially for automotive coatings. However, potential toxicity caused by aluminum in humans limits the used of this metal. 관-alumina is the most stable form of aluminum in various phases. Although the results of studies evaluating the dermal toxicity of 관-alumina remained unclear, this compound can still be used as a pigment in cosmetics for humans. In the current study, we further evaluated the dermal cytotoxic effects of 관-alumina on human skin cells and an in vivo mouse model. We also measured the in vitro penetration profile of flake-like 관-alumina in porcine skin and assessed the degree of cellular metabolic disorders. Our findings demonstrated that treatment with flake-like 관-alumina did not significantly affect cell viability up to 24 h. This compound was found to have a non-penetration profile based on a Franz modified diffusion cell assay. In addition, flake-like 관-alumina was not found to induce dermal inflammation as assessed by histology of epidermal architecture, hyperplasia, and the expression of Interleukin-1관 and Cyclooxygenase-2. Results of the cellular metabolic disorder assay indicated that flake-like 관-alumina does not exert a direct effect on human skin cells. Taken together, our findings provided not only evidence that flake-like 관-alumina may serve as a pearlescent pigment in cosmetics but also experimental basis utilizing 관-alumina for human application. Our results also obviously provide new insight of the further toxicity study to aluminum based nanoparticles for skin.</P>
Design of Falling Recognition Application System using Deep Learning
TaeWoo Kwon,Jong-Yong Lee,Kye-Dong Jung 한국인터넷방송통신학회 2020 International Journal of Internet, Broadcasting an Vol.12 No.2
Studies are being conducted regarding falling recognition using sensors on smartphones to recognize falling in human daily life. These studies use a number of sensors, mostly acceleration sensors, gyro sensors, motion sensors, etc. Falling recognition system processes the values of sensor data by using a falling recognition algorithm and classifies behavior based on thresholds. If the threshold is ambiguous, the accuracy will be reduced. To solve this problem, Deep learning was introduced in the behavioral recognition system. Deep learning is a kind of machine learning technique that computers process and categorize input data rather than processing it by man-made algorithms. Thus, in this paper, we propose a falling recognition application system using deep learning based on smartphones. The proposed system is powered by apps on smartphones. It also consists of three layers and uses DataBase as a Service (DBaaS) to handle big data and address data heterogeneity. The proposed system uses deep learning to recognize the user's behavior, it can expect higher accuracy compared to the system in the general rule base.
Design of Cloud-based Context-aware System Based on Falling Type
Kwon, TaeWoo,Lee, Jong-Yong,Jung, Kye-Dong The Institute of Internet 2017 International Journal of Internet, Broadcasting an Vol.9 No.4
To understand whether Falling, which is one of the causes of injuries, occurs, various behavior recognition research is proceeding. However, in most research recognize only the fact that Falling has occurred and provide the service. As well as the occurrence of the Falling, the risk varies greatly based on the type of Falling and the situation before and after the Falling. Therefore, when Falling occurs, it is necessary to infer the user's current situation and provide appropriate services. In this paper, we propose to base on Fog Computing and Cloud Computing to design Context-aware System using analysis of behavior data and process sensor data in real-time. This system solved the problem of increase latency and server overload due to large capacity sensor data.
TaeWoo Kwon,Daepyo Lee,Jong-Yong Lee,Kye-Dong Jung 한국인터넷방송통신학회 2018 International Journal of Internet, Broadcasting an Vol.10 No.3
The majority of existing falling recognition techniques provide service by recognizing only that the falling occurred. However, it is important to recognize not only the occurrence of falling but also the situation before and after the falling, as well as the location of the falling. In this paper, we design and propose the falling notification service system to recognize and provide service. This system uses the acceleration sensor of the smartphone to recognize the occurrence of a falling and the situation before and after the falling. In order to check the location of falling, GPS sensor data is used in the Google Map API to map to the map. Also, a crosswalk map converted into grid-based coordinates based on the longitude and latitude of the crosswalk is stored, and the locations before and after falling are mapped. In order to reduce the connection speed and server overload for real-time data processing, fog computing and cloud computing are designed to be distributed processing.