Pusan National University

About: Pusan National University is a education organization based out in Busan, South Korea. It is known for research contribution in the topics: Catalysis & Population. The organization has 24124 authors who have published 45054 publications receiving 819356 citations. The organization is also known as: Busan National University & Pusan University.

Characterization of FBG sensor interrogation based on a FDML wavelength swept laser

Abstract: In this study, we develop an ultra-fast fiber Bragg grating sensor system that is based on the Fourier domain mode-locked (FDML) swept laser. A FDML wavelength swept laser has many advantages compared to the conventional wavelength swept laser source, such as high-speed interrogation, narrow spectral sensitivity, and high phase stability. The newly developed FDML wavelength swept laser shows a superior performance of a high scan rate of 31.3 kHz and a broad scan range of over 70 nm simultaneously. The performance of the grating sensor interrogating system using a FDML wavelength swept laser is characterized in both static and dynamic strain responses.

Corrigendum: 2018 Korean Society for the Study of Obesity Guideline for the Management of Obesity in Korea

Abstract: [This corrects the article on p. 40 in vol. 28, PMID: 31089578.].

A Multidirectional LSTM Model for Predicting the Stability of a Smart Grid

Abstract: The grid denotes the electric grid which consists of communication lines, control stations, transformers, and distributors that aids in supplying power from the electrical plant to the consumers. Presently, the electric grid constitutes humongous power production units which generates millions of megawatts of power distributed across several demographic regions. There is a dire need to efficiently manage this power supplied to the various consumer domains such as industries, smart cities, household and organizations. In this regard, a smart grid with intelligent systems is being deployed to cater the dynamic power requirements. A smart grid system follows the Cyber-Physical Systems (CPS) model, in which Information Technology (IT) infrastructure is integrated with physical systems. In the scenario of the smart grid embedded with CPS, the Machine Learning (ML) module is the IT aspect and the power dissipation units are the physical entities. In this research, a novel Multidirectional Long Short-Term Memory (MLSTM) technique is being proposed to predict the stability of the smart grid network. The results obtained are evaluated against other popular Deep Learning approaches such as Gated Recurrent Units (GRU), traditional LSTM and Recurrent Neural Networks (RNN). The experimental results prove that the MLSTM approach outperforms the other ML approaches.

Radiation-induced liver disease: current understanding and future perspectives

Abstract: Although radiotherapy (RT) is used for the treatment of cancers, including liver cancer, radiation-induced liver disease (RILD) has emerged as a major limitation of RT. Radiation-induced toxicities in nontumorous liver tissues are associated with the development of numerous symptoms that may limit the course of therapy or have serious chronic side effects, including late fibrosis. Although the clinical characteristics of RILD patients have been relatively well described, the understanding of RILD pathogenesis has been hampered by a lack of reliable animal models for RILD. Despite efforts to develop suitable experimental animal models for RILD, current animal models rarely present hepatic veno-occlusive disease, the pathological hallmark of human RILD patients, resulting in highly variable results in RILD-related studies. Therefore, we introduce the concept and clinical characteristics of RILD and propose a feasible explanation for RILD pathogenesis. In addition, currently available animal models of RILD are reviewed, focusing on similarities with human RILD and clues to understanding the mechanisms of RILD progression. Based on these findings from RILD research, we present potential therapeutic strategies for RILD and prospects for future RILD studies. Therefore, this review helps broaden our understanding for developing effective treatment strategies for RILD.