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QoE-based Reinforcement Learning at MEC for Improving 4K Streaming Quality
Sangwon Oh,Jinho Jang,Pyungkoo Park 한국디지털콘텐츠학회 2019 The Journal of Contents Computing Vol.1 No.1
4K or ultra-high-definition (UHD) will be the standard for video streaming in the next decade. In this research, we carry a study on dynamic adaptive streaming over HTTP (DASH) which is a crucial adaptive algorithm. More specifically, we employ asynchronous reinforcement learning (RL) algorithm in the form of quality of experience (QoE). In fact, QoE is an important factor to evaluate the efficiency of streaming transmission models. Hence, we made every streaming request must be passed through a MEC server, and the MEC make a decision to retrieve segments for clients. Experiment result shows that our proposed QoE RL algorithm achieves higher quality compared to our previous research on the same context of content-awareness.
Measuring T1 contrast in ex-vivo prostate tissue at the Earth’s magnetic field
Sangwon Oh,한재호,Ji Eun Kwon,심정현,이성주,황성민,Ingo Hilschenz,김기웅 한국자기공명학회 2019 Journal of the Korean Magnetic Resonance Society Vol.23 No.1
A former study has shown that the spin-lattice relaxation time (T1) in cancerous prostate tissue had enhanced contrast at an ultra-low magnetic field, 132 uT. To study the field dependence and the origin of the contrast we measured T1 in pairs of ex-vivo prostate tissues at the Earth’s magnetic field. A portable and coil-based nuclear magnetic resonance (NMR) system was adopted for T1 measurements at 40 uT. The T1 contrast, delta = 1 - T1 (more cancer)/T1(less cancer), was calculated from each pair. Additionally, we performed pathological examinations such as Gleason’s score, cell proliferation index, and micro-vessel density (MVD), to quantify correlations between the pathological parameters and T1 of the cancerous prostate tissues.
Mini-review on fabrication of nitrogen vacancy center in diamond and its application to NMR
Oh, Sangwon 한국자기공명학회 2019 Journal of the Korean Magnetic Resonance Society Vol.23 No.3
Nitrogen-vacancy (NV) is one of the most popular solid-state spin systems for quantum sensing. NV has been used for vector magnetometry with nanometer spatial resolution and sensors for nuclear magnetic resonance (NMR) in samples with small volume, less than 10 pL. Various studies are in progress to make NV a complementary sensor for current NMR technique. Fabricating and improving diamond itself are one of the research topics. This mini-review contains recent develops in diamond fabrication and treatment for higher NV yield. Additionally, we briefly introduce the development status of NV in NMR.
Mini-review on fabrication of nitrogen vacancy center in diamond and its application to NMR
Sangwon Oh 한국자기공명학회 2019 Journal of the Korean Magnetic Resonance Society Vol.23 No.3
Nitrogen-vacancy (NV) is one of the most popular solid-state spin systems for quantum sensing. NV has been used for vector magnetometry with nanometer spatial resolution and sensors for nuclear magnetic resonance (NMR) in samples with small volume, less than 10 pL. Various studies are in progress to make NV a complementary sensor for current NMR technique. Fabricating and improving diamond itself are one of the research topics. This mini-review contains recent develops in diamond fabrication and treatment for higher NV yield. Additionally, we briefly introduce the development status of NV in NMR.
Measuring T<sub>1</sub> contrast in ex-vivo prostate tissue at the Earth's magnetic field
Oh, Sangwon,Han, Jae Ho,Kwon, Ji Eun,Shim, Jeong Hyun,Lee, Seong-Joo,Hwang, Seong-Min,Hilschenz, Ingo,Kim, Kiwoong Korean Magnetic Resonance Society 2019 Journal of the Korean Magnetic Resonance Society Vol.23 No.1
A former study has shown that the spin-lattice relaxation time ($T_1$) in cancerous prostate tissue had enhanced contrast at an ultra-low magnetic field, $132{\mu}T$. To study the field dependence and the origin of the contrast we measured $T_1$ in pairs of ex-vivo prostate tissues at the Earth's magnetic field. A portable and coil-based nuclear magnetic resonance (NMR) system was adopted for $T_1$ measurements at $40{\mu}T$. The $T_1$ contrast, ${\delta}=1-T_1$ (more cancer)/$T_1$(less cancer), was calculated from each pair. Additionally, we performed pathological examinations such as Gleason's score, cell proliferation index, and micro-vessel density (MVD), to quantify correlations between the pathological parameters and $T_1$ of the cancerous prostate tissues.
Methods of Generating Time Series Image for detecting Anomalies in Time Series data
Sangwon Oh,Seungmin Oh,Yeonggwang Kim,Junchurl Yoon,Tai-Won Um 한국디지털콘텐츠학회 2021 The Journal of Contents Computing Vol.3 No.2
To detect anomalies in time series data, statistical techniques such as PCA and autoencoder are used, or anomalies are detected based on deep learning models such as RNN. It is difficult to expect good performance only with simple statistical techniques or RNN-based deep learning models because the environment and causes in which anomalies are recorded are not simple and various variables affect them. In this paper, we proposed a method of detecting anomalies using CNN-based deep learning model, a binary classification model of representative images, by imaging time series data. RP, GASF, GADF, and MAF algorithms were used as methods for imaging. All of time series image-based models showed equal or higher accuracy than conventional LSTM-based models, and among imaging-based CNN models, the method of imaging with MTF algorithm derived the highest accuracy.
Application TadGAN to Detect Collective Anomaly in Power Usage Data
Sangwon Oh,Md Rashedul Islam 한국디지털콘텐츠학회 2021 The Journal of Contents Computing Vol.3 No.1
In order to develop an efficient power generation plan, it is necessary to identify consumers’ power usage patterns. In general, power usage data takes the form of time series data and in order to analyze that data, it is necessary to first verify that there is no data contamination. To this end, the process of verifying that there are no anomalies in the data is essential. In particular, for power data, anomalies are often recorded across multiple time units rather than just one point. In this work, we applied the TadGAN algorithm to detect these collective anomalies. Using the power usage data recorded in the actual building, the anomalies were injected randomly with various conditions. Afterwards, we detected anomalies by using TadGAN and showed that we were better at detecting collective anomaly than point anomaly.
A Study on Super Resolution of SRCNN according to Early Up-sampling Method
Seungmin Oh,Sangwon Oh,Hyeju Shin,Jinsul Kim 한국디지털콘텐츠학회 2021 The Journal of Contents Computing Vol.3 No.2
Super resolution is a technology that converts low-resolution images into high-resolution images, and super resolution technologies using deep learning have recently been studied. The SRCNN model was first studied as a deep learning-based super-resolution technology, and the SRCNN model performs early up-sampling and learning using interpolation to perform super-resolution. In this paper, the best performance early up-sampling interpolation was investigated through the performance comparison of the early up-sampling method. Through this, it was found that the bicubic interpolation method derived good performance from up to 35% to at least 17% compared to other interpolation methods.
환경정의 관점에서 본 폭염 취약 지역과 사회⋅취약계층 간의 공간적 패턴 분석
오상원(Sangwon Oh),하동오(Dongoh Ha),정주철(Juchul Jung) 한국방재학회 2023 한국방재학회논문집 Vol.23 No.4
오늘날 전 세계는 기후변화로 인한 일 최고기온 증가, 폭염일수의 증대 등으로 인해 인명피해 및 피해액이 지속적으로 상승하는추세이다. 이에 따라 폭염으로 인한 취약계층에 대한 고려가 필수적이며, 실질적으로 큰 피해가 예상되는 취약계층에 대한기준이 필요하다. 그러므로 본 연구는 시간 및 공간적 범위로는 2010년부터 2018년까지의 전국 시군구들을 대상으로 폭염취약성 지수에 따른 폭염 취약지역을 선정하고, moran’s I 공간자기상관 분석 및 LISA군집지도 분석을 통한 공간적 분석을 진행하였다. 본 연구는 사회적 취약계층 거주 및 활동 지역과 폭염 위험지역과의 공간적 상관관계를 확인하고 이에 무더위쉼터정책의 적용에 대하여 환경 부정의 지역을 도출하였다. Currently, the number of human casualties and damages is continuously increasing owing to increases in the daily maximum temperature and number of heat wave days caused by climate change. Therefore, considering the class vulnerable to heat waves and setting standards for the vulnerable class expected to suffer substantial damage are essential. Therefore, in terms of time and space, this study selected heatwave-vulnerable areas according to the heatwave vulnerability index, targeting cities, counties, and districts nationwide from 2010 to 2018, and performed spatial analysis using Moran's I spatial autocorrelation analysis and LISA cluster map analysis. proceeded. The spatial correlation between socially vulnerable living and activity areas and the heat wave risk area was confirmed, and the area of environmental negativity for applying an extreme heat shelter policy was derived.