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박상욱,오선호,박수완,임경수,최범석,박소희,김상원,한승완,한종욱,김건우,Park, Sangwook,Oh, Seon Ho,Park, Su Wan,Lim, Kyung Soo,Choi, Bum Suk,Park, So Hee,Ghyme, Sang Won,Han, Seung Wan,Han, Jong-Wook,Kim, Geonwoo 한국전자통신연구원 2020 전자통신동향분석 Vol.35 No.2
Predicting where and when a crime may occur in an area of interest is one of many strategies of predictive policing. Multidimensional analysis, including CCTV, can overcome the limitations of hotspot prediction, especially of violent crimes. In order to identify the precursors of a crime, it is necessary to analyze dynamic data such as attributes and activities of people, social information, environmental information, traffic flows, and weather. These parameters can be recognized by CCTV. In addition, it provides accurate analysis of the circumstances of a crime in a dynamic situation, calculates the risk, and predicts the probability of a crime occurring in the near future. Additionally, it provides ways to gather historical criminal datasets, including sensitive personal information.
박상욱,신정우,이무형,김태욱,Park, Sang-Wook,Shin, Jeong-Woo,Lee, Mu-Hyoung,Kim, Tae-Uk 한국항공운항학회 2015 한국항공운항학회지 Vol.23 No.1
In order to increase endurance flight efficiency of long endurance electric powered UAV, main wing of UAV should have high aspect ratio and low structural weight. Since a spar which consists of thin and slender structure for weight reduction can cause catastrophic failure during the flight, it is important to develop verification method of structural integrity of the spar with the light weight design. In this paper, process of structural analysis using non-linear finite element method was introduced for the verification of structural integrity of the spar. The static strength test of the spar was conducted to identify structural characteristic under the static load. Then, the experimental result of the spar was compared to the analytical result from the non-linear finite element analysis. It was found that the developed process of structural analysis could predict well the non-linear structural behavior of the spar under ultimate load.
Chemical Reaction of Carbon Dioxide with AMP in w/o Emulsion Membrane
박상욱,최병식,김성수,이재욱,Park Sang-Wook,Choi Byoung-Sik,Kim Seong-Soo,Lee Jae-Wook The Membrane Society of Korea 2004 멤브레인 Vol.14 No.4
본 연구에서는 준 회분식 교반조를 사용하여 polybutene (PB)와 polyisobutylene (PIB)고분자를 용해한 벤젠 용액을 연속상, 물을 불연속상으로 구성한 w/o 에멀션액막에 $CO_2$을 흡수시켜 흡수속도를 측정하였다. 점탄성을 나타내는 Deborah 수를 사용하여 점탄성 비뉴튼액체에서 구한 부피물질전달계수 ($k_La$)를 고찰하고, 수용액에 첨가한 2-amino-2-methyl-1-propanol(AMP)와 $CO_2$의 반응 메카니즘을 해석하였다. Carbon dioxide was absorbed into water-in-oil (w/o) emulsion composed of aqueous 2-amino-2-methyl-1-propanol (AMP) droplets as a dispersed phase and benzene solutions of polybutene and polyisobutylene as a continuous phase in a flat-stirred vessel to investigate the effect of non-Newtonian rheological behavior on the rate of chemical absorption of $CO_2$, where the reaction between $CO_2$ and AMP in the aqueous phase was assumed to be a pseudo-first-order reaction. It was expressed that PIB with elastic property made the rate of chemical absorption of $CO_2$ accelerated by comparison of mass transfer coefficient of $CO_2$ in the non-Newtonian liquid with that in the Newtonian liquid.
박상욱,최우현,고한석,Park, Sangwook,Choi, Woohyun,Ko, Hanseok 한국음향학회 2016 韓國音響學會誌 Vol.35 No.1
동일한 장소에서도 매우 다양한 음향이 발생하고, 서로 다른 장소에서도 유사한 음향이 발생하기 때문에 훈련 데이터가 적거나, 훈련 단계에서 일부 음향만 고려된 경우 음향 상황 인지 성능을 보장할 수 없다. 이러한 문제점을 해결하기 위한 방법으로 Bag of Words (BOW) 기반 히스토그램 특징이 소개되었다. 하지만 BOW 기반 히스토그램 특징은 일정 시간동안 발생한 음향의 분포를 이용하기 때문에 음향이 발생한 순차적인 정보는 고려할 수 없다. 음향 상황 인지에서 일정 시간 동안 발생한 음향의 주기성과 지속성은 상황을 인지하는데 중요한 정보가 될 수 있다. 따라서 본 논문에서는 재발량 분석을 이용하여 주기성과 지속성에 대한 특징을 추출하였다. 인식 실험에서 재발량 분석을 통해 추출된 특징을 함께 사용한 경우 기존 방법들 보다 향상된 성능을 확인했다. Since a variety of sound occur in same place and similar sound occurs in other places, the performance of acoustic scene classification is not guaranteed in case of insufficient training data. A Bag of Words (BOW) based histogram feature is foreseen as a method to overcome the problem. However, since the histogram features is made by using a feature distribution, the ordering of sequence of features is ignored. A temporal information such as periodicity and stationarity are also important for acoustic scene classification. In this paper, temporal features about a periodicity and a stationarity are extracted by using a recurrent quantification analysis. In the experiment, performance of the proposed method is shown better than other baseline methods.