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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.
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.
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.
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.
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.
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.
시계열 데이터의 이상 탐지를 위한 Recurrence Plot 알고리즘 기반 시계열 이미지 생성 방안
오상원(Sangwon Oh),윤준철(Junchul Yoon),김영관(Youngkwan Kim),김진술(Jinsul Kim) 한국통신학회 2022 한국통신학회 학술대회논문집 Vol.2022 No.2
시계열 데이터에서 이상치를 탐지하기 위해서는 PCA나 오토인코더와 같은 통계적 기법을 사용하거나 RNN 같은 딥러닝 모델을 기반으로 이상치를 탐지한다. 그러나 이상치가 기록되는 환경 및 원인이 단순하지 않고 다양한 변인이 영향을 미치기 때문에 간단한 통계적 기법 또는 RNN 기반 딥러닝 모델만으로 좋은 성능을 기대하기 어렵다. 본 논문에서는 시계열 데이터를 이미지화시켜서 대표적인 이미지의 이진(Binary) 분류 모델인 CNN 기반의 딥러닝 모델을 사용하여 이상치를 탐지하는 방법을 제안하였고 기존 LSTM 기반 모델보다 0.01 높은 F1-Score를 보여줌으로써 동등하거나 더 높은 성능을 도출하였다.