BEGIN:VCALENDAR VERSION:2.0 PRODID:-//CERN//INDICO//EN BEGIN:VEVENT SUMMARY:복소 산술 신경망을 통한 물리적 기계학습모형의 건설과 물리적 불변량의 추출 DTSTART;VALUE=DATE-TIME:20191108T074000Z DTEND;VALUE=DATE-TIME:20191108T075500Z DTSTAMP;VALUE=DATE-TIME:20260725T113622Z UID:indico-contribution-8-3@sshep.snu.ac.kr DESCRIPTION:Speakers: Won Sang Cho (Seoul National University)\nhttps://ss hep.snu.ac.kr/event/107/contributions/3/ LOCATION:Seoul National University\, College of Natural Sciences\, Buildin g 25-1 국제회의실 (International Conference Hall) URL:https://sshep.snu.ac.kr/event/107/contributions/3/ END:VEVENT BEGIN:VEVENT SUMMARY:Probing Trilinear Higgs Self–coupling at the HL-LHC with Machine Learning DTSTART;VALUE=DATE-TIME:20191108T064000Z DTEND;VALUE=DATE-TIME:20191108T065500Z DTSTAMP;VALUE=DATE-TIME:20260725T113622Z UID:indico-contribution-8-15@sshep.snu.ac.kr DESCRIPTION:Speakers: Jubin Park (Institute for Universe and Elementary Pa rticles\, Chonnam National University)\n1964년\, 피터 힉스에 의해 제안된 힉스 메커니즘을 통해 힉스입자의 존재가 예측 되었다. 이후 48년이 지나\, 2012년 7월 유럽입자물리연구 소(CERN) 대형강입자 충돌기(LHC)에서 드디어 이 입자가 발 견되었다. 현재 이 입자의 성질을 정밀하게 측정하고 검 증하고자\, LHC에서 다양한 연구들이 진행 중에 있다. 이 발표에서는 이 중\, 힉스입자의 삼중 커플링의 검측 성 능을 머신러닝의 기술을 이용해 개선한 예를 통해\, 고 에너지 물리학에서의 응용을 소개하고자 한다.\n\nhttps:// sshep.snu.ac.kr/event/107/contributions/15/ LOCATION:Seoul National University\, College of Natural Sciences\, Buildin g 25-1 국제회의실 (International Conference Hall) URL:https://sshep.snu.ac.kr/event/107/contributions/15/ END:VEVENT BEGIN:VEVENT SUMMARY:Application of Neural Network Model to Predict and to Evaluate the Groundwater Level Fluctuation DTSTART;VALUE=DATE-TIME:20191108T072500Z DTEND;VALUE=DATE-TIME:20191108T074000Z DTSTAMP;VALUE=DATE-TIME:20260725T113622Z UID:indico-contribution-8-17@sshep.snu.ac.kr DESCRIPTION:Speakers: Sanghoon Lee (Seoul National University)\nGroundwate r is one of valuable water resources used for the various purposes in our life\, but it is not limitless. For this reason\, prediction and managemen t of the groundwater are very essential work for sustainable use of it. Ph ysics-based models are usually applied on prediction of groundwater level\ , but they are hard to be implemented successfully when there is any unkno wn physical property or when subterranean structure is very complicated. I n this research\, prediction of the groundwater level at riverside area in Yangpyeong\, Korea was carried out using neural network model instead. In study area where several natural and anthropogenic factors affect the gro undwater level fluctuation\, groundwater levels at 8 monitoring wells were well predicted with low range of RMSE errors. Moreover\, monthly contribu tions\, which indicate the impact of input variables\, were computed to fi gure out the seasonal variance of influencing factors. This study could su ggest another option to predict the groundwater level\, and help understan ding spatial and temporal variation of impacts of factors affecting the gr oundwater level fluctuation.\n\nAcknowledgement: This work was supported b y the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIP) (No. 2017R1A2B3002119)\n\nhttps://sshep.snu.ac.kr/event/ 107/contributions/17/ LOCATION:Seoul National University\, College of Natural Sciences\, Buildin g 25-1 국제회의실 (International Conference Hall) URL:https://sshep.snu.ac.kr/event/107/contributions/17/ END:VEVENT BEGIN:VEVENT SUMMARY:Prediction and Taking Insight of Molecular Quantum Property by Ran dom Forest Regression DTSTART;VALUE=DATE-TIME:20191108T065500Z DTEND;VALUE=DATE-TIME:20191108T071000Z DTSTAMP;VALUE=DATE-TIME:20260725T113622Z UID:indico-contribution-8-22@sshep.snu.ac.kr DESCRIPTION:Speakers: Beomchang Kang (Seoul National University)\nFluoresc ent molecules are widely used for bio-imaging. They are attached to specif ic cell organelles or proteins\, enabling observation of detailed structur e and dynamics in the cell. Efficient fluorescent molecules must have a hi gh quantum yield for effective bio-imaging. Diverse effective fluorescent molecules whose color are distinctive need to get more information of cell or protein. First step of discover novel molecules using computational ap proach is prediction of compound. Here\, we use random forest regression t o predict excitation energy and oscillator strength of a molecule. Random forest algorithm is white box. It is easy to extract feature importance. W e could get insight from it.\nA statistical machine that predicts excitati on energies and associated oscillator strengths\, the probability of absor ption or emission of light in transitions between different energy states\ , of a molecule were trained using the random forest algorithm. The Pubche mQC database was used as a training set. It has over 3 million known compo unds. We picked up 0.5 million molecules randomly. 90% of them were in tr aining set and the others were test set. The ECFP4(extended connectivity f ingerprints 2) of molecules were used as input features. We found some fra gments which can decide molecules’ quantum property by feature importanc e analysis.\n\nhttps://sshep.snu.ac.kr/event/107/contributions/22/ LOCATION:Seoul National University\, College of Natural Sciences\, Buildin g 25-1 국제회의실 (International Conference Hall) URL:https://sshep.snu.ac.kr/event/107/contributions/22/ END:VEVENT BEGIN:VEVENT SUMMARY:Predicting location of suitable groundwater for brewing coffee usi ng tree-based ensemble machine learning DTSTART;VALUE=DATE-TIME:20191108T071000Z DTEND;VALUE=DATE-TIME:20191108T072500Z DTSTAMP;VALUE=DATE-TIME:20260725T113622Z UID:indico-contribution-8-23@sshep.snu.ac.kr DESCRIPTION:Speakers: Hye-Lim Lee (School of Earth and Environmental Scien ces\, Seoul National University)\nRecently\, groundwater is used as a sour ce of drinking purposes such as water\, coffee\, beer and other beverages. The range of water quality for producing high-quality of beverages is dif ferent from each usage\, so it is important to find suitable groundwater l ocation for each purpose in the aspect of water industry. This study was c onducted to predict the suitable location for brewing coffee in Gangwon Pr ovince\, South Korea using tree-based ensemble machine learning. Appropria te water quality standard for brewing coffee is known as TDS of 75~250 mg/ L and calcium hardness of 17~85 mg/L from recent research. Boosted Regress ion Trees (BRT)\, Random Forests (RF) and Extremely Randomized Trees (ERT) were used as tree-based ensemble method. Response indicating suitable or unsuitable groundwater for brewing coffee was determined by 254 wells’ w ater quality data\, and predictor variables were composed of slope\, altit ude\, drainage grade\, effective soil depth\, soil composition\, land use\ , and hydrogeology based on GIS data. Applying models to the test data\, a ll three models showed the area under a curve (AUC) and accuracy more than 0.85 and 0.80\, respectively\, which indicates high reliability in predic tion. Threshold of dividing suitable or unsuitable for brewing coffee is f ound from Receiver Operating Characteristic (ROC) curve. The BRT showed th e highest AUC and accuracy among the three models\, therefore\, potential map of suitable groundwater location for brewing coffee was suggested by t he BRT model. In the condition of lack of water quality data\, this resear ch can help to determine location of suitable groundwater for several usag es.\nKeyword: potable groundwater · boosted regression tree · random for est · extremely randomized tree \nAcknowledgement: This research was supp orted by the National Research Council of Science and Technology(NST) gran t funded by the Korea government(MSIP) (No. CAP-17-05-KIGAM)\n\nhttps://ss hep.snu.ac.kr/event/107/contributions/23/ LOCATION:Seoul National University\, College of Natural Sciences\, Buildin g 25-1 국제회의실 (International Conference Hall) URL:https://sshep.snu.ac.kr/event/107/contributions/23/ END:VEVENT END:VCALENDAR