Non-face-to-face online education which suddenly began with COVID-19, was an opportunity to expand to university education sites. This study presents online classes based on the Tandem learning method as examples and examines the class operation, learning content, and learner satisfaction according to the actual type of flip learning classes and real-time video classes. By converting Tandem classes operated by B University in Busan into online classes, the existing e-Tandem classes were expanded using flip learning and ZOOM. The first Flip learning class allows self-directed learning, and the free form of learning through pre-learning becomes an advantage of the online class type. However, while professors participate in the small conference room and feedback, there is a need to be supplemented with the ability to feedback directly to other teams. Furthermore, as a result of the learner satisfaction survey, there were complaints about prior learning and the amount of tasks, so studies on specific tasks and the content and methods of prior learning are also needed. The second real-time video class allows interaction between professors and learners, learners and learners. The biggest feature of this class type is that it can solve the absence of communication, which was a disadvantage of non-face-to-face classes. However, the ability of a professor is needed to conduct a real-time video class like this. Unlike learners who are familiar with digital technology, only when they understand and learn various online content and functions will their online classes become as natural as face-to-face classes.

This paper presents a real-time, false-pick filter based on deep learning to reduce false alarms of an onsite Earthquake Early Warning (EEW) system. Most onsite EEW systems use P-wave to predict S-wave. Therefore, it is essential to properly distinguish P-waves from noises or other seismic phases to avoid false alarms. To reduce false-picks causing false alarms, this study made the EEWNet Part 1 'False-Pick Filter' model based on Convolutional Neural Network (CNN). Specifically, it modified the Pick_FP (Lomax et al.) to generate input data such as the amplitude, velocity, and displacement of three components from 2 seconds ahead and 2 seconds after the P-wave arrival following one-second time steps. This model extracts log-mel power spectrum features from this input data, then classifies P-waves and others using these features. The dataset consisted of 3,189,583 samples: 81,394 samples from event data (727 events in the Korean Peninsula, 103 teleseismic events, and 1,734 events in Taiwan) and 3,108,189 samples from continuous data (recorded by seismic stations in South Korea for 27 months from 2018 to 2020). This model was trained with 1,826,357 samples through balancing, then tested on continuous data samples of the year 2019, filtering more than 99% of strong false-picks that could trigger false alarms. This model was developed as a module for USGS Earthworm and is written in C language to operate with minimal computing resources.

In this study, acoustic and viscosity data are collected in real time during the ball milling process and analyzed for correlation. After fast Fourier transformation (FFT) of the acoustic data, changes in the signals are observed as a function of the milling time. To analyze this quantitatively, the frequency band is divided into 1 kHz ranges to obtain an integral value. The integrated values in the 2–3 kHz range of the frequency band decrease linearly, confirming that they have a high correlation with changes in viscosity. The experiment is repeated four times to ensure the reproducibility of the data. The results of this study show that it is possible to estimate changes in slurry properties, such as viscosity and particle size, during the ball milling process using an acoustic signal.

PURPOSES : For large-scale construction, such as a concrete pavement, design and construction are not entirely consistent. If the inconsistency between design and construction is very large, construction quality is significantly degraded, affecting performance life span and driving comfort. The quality of pavement construction is managed according to standards. However, it is difficult to improve construction quality as the standard measures construction quality after construction is completed. Therefore, this study developed a system to measure the construction quality of concrete pavement in real-time and presented the corresponding standards. METHODS : A basic module for simultaneously measuring the width, thickness, and roughness of the concrete pavement was designed. Based on the measurement results of the distance measurement sensor, a calibration method is presented that can remove noise. The system process was developed to measure construction quality based on location and distance data, measured in real-time using GPSs and sensors. The field application experiment was conducted and the results were analyzed. RESULTS : The measurement module is properly designed to be used in concrete pavement construction sites. Noise was removed from the distance measurement sensor results according to the presented calibration method, leaving only the wave of pavement surface irregularities. As a result of applying the system process in the field application, a reasonable level of PRI was observed. CONCLUSIONS : In the past, the width, thickness, and roughness were measured after construction was completed and, if the standard was not met, construction quality control was performed via reconstruction or repair. Through this study, it is expected that the width, thickness, and roughness of the concrete pavement can be measured in real-time and, if the standard is not met, construction quality can be immediately controlled during construction to maintain high quality.

본 연구는 스마트건설 지원을 위한 드론 활용의 활성화를 위해 RTK 드론 기반의 항공측량 정밀도를 분석하고자 GPS만을 사용하는 방식, GCP를 설치하는 방식, RTK 드론을 이용한 방식의 정사영상의 위치정확도를 분석하였고 사업의 목적과 대상지의 형태에 따른 드론 활용의 기준을 제시하였다. 또한 상용 드론을 이용한 체적기반의 토공량 산출을 2.5D 환경에서 산출하여 기존 방법과 비교해서 드론영상을 효율적으로 활용할 수 있는 방법을 제시하였다. 본 연구로 대규모 건설현장의 작업효율 및 드론 활성화가 기대된다.

적조가 처음 시작되는 해역을 조기에 파악하기 위하여 Quantitative real-time PCR (qPCR)을 경남해역 적조현장에 활용하였다. 2019년 경남해역을 대상으로 Cochlodinium polykrikoides를 qPCR로 정량분석한 결과, 6월 초에 저밀도로(0.0015~0.0058 cells mL-1) 검출되기 시작하여 8월 중순에는 최대 0.163 cells mL-1 밀도로 증가하였고, 주로 남해도 주변에서 높게 검출되었다. 8월 말에는 현미경 검경으로 남해도 주변에서 높게 출현함이 확인되었고(최대 24 cells mL-1), 9월 2일에는 남해도에서 적조주의보가 발령되었고(최대 200 cells mL-1), 9월 11일에는 최대 12,000 cells mL-1까지 남해도 해역에서 발생하였다. 위 결과는 극미량의 C. polykrikoides이 적조발생 전에 남해도에서 검출 되었고 이후 같은 해역에서 적조가 발생되었음을 보여준다. 이는 qPCR이 극미량의 C. polykrikoides을 조기검출하는데 유용한 방법임을 보여준다.

2020년 1학기 코로나19로 인해 모든 대학들이 불가피하게 온라인 강의를 진행했다. 그와 함께 온라인 강의에 대한 관심이 급격히 높아져갔다. 하지만 대대적인 온라인 강의는 이번이 처음이어서인지 그에 관한 기존 연구가 거의 없는 실정이다. 필자들은 2020년도 1학기 고려대학교 <글쓰기Ⅰ> 과목을 온라인 실시간 강의로 진행했을 뿐만 아니라, 학기 말에는 모든 학생들을 대상으로 그에 관한 설문조사를 실시하 기도 했다. 본고는 그러한 갖가지 수업자료와 설문조사를 토대로 온라인 강의, 특히 실시간 강의의 운영 방법과 교육 효과에 대해 자세히 분석해본 것이다. 그리하여 온라인 강의의 운영 사례를 공유하는 한편, 온라인 교육의 질을 높이는 데 조금이나마 기여하고자 했다.

본 연구는 양액 내 존재하는 다량 영양소의 농도를 실시간으로 측정하기 위해 이온 선택 전극 (ISE) 으로 구성된 임베디드 시스템의 개발을 보여준다. NO3, K 및 Ca 이온을 감지하기위한 PVC ISE, H2PO4를 감지하기위한 코발트 전극, 기준 전극, 샘플 용액이 담기는 챔버, 펌프 및 밸브를 사용하여 측정하는 시스템으로 구성된다. 양액 샘플양 조절과 데이터 수집을 위해서 데이터 Due 보드가 사용되었고, 각각의 샘플 측정 전에, 측정 중 발생하는 드리프트를 최소화시키기 위해 2 점 정규화 방법을 사용하였다. PVC 멤브레인을 기반으로 한 NO3 및 K 전극의 농도 예측 성능은 표준 분석기의 결과와 근접한 일치 (R2 = 0.99) 나타내며 만족스러운 결과를 나타냈다. 하지만, Ca Ⅱ 이온 투과체 제조된 Ca 전극은 고농도 양액 농도에서 Ca 농도를 55 %로 낮게 측정하였다. 코발트 전극 기반 인산 측정은 반복측정 중에 발생한 코발트 전극의 불안정한 신호로 인해 표준 방법과 비교하여 45 ~ 155 mg / L의 인산 농도 범위에서 24.7 ± 9.26 %의 비교적 높은 오차를 나타냈다. 수경 P 감지의 예측 능력을 향상시키기 위해 코발트 전극의 신호 컨디셔닝에 대한 추가 연구가 필요함으로 판단된다.

Many companies make considerable efforts in implementing supply chain management systems to increase their competitiveness. In particular, there are significant investments in their ICT (Information and Communication Technology) and supply chain. However, considerable diversity exists in how well firms have been able to assimilate ICT and leverage the business value of it. In addition, studies on supply chains, particularly Real Time Enterprise (RTE) competence, which is cited for rapid information sharing and dynamic capabilities for each role in the supply chain, are insufficient. The purpose of this paper is to explore and evaluate effect of ICT utilization capabilities, including ‘ICT internal cooperative capabilities’ and ‘ICT operational flexibility capabilities’, on RTE capabilities (flexibility, visibility and prediction) and supply chain performance. To validate the research model proposed in the study, survey was conducted on companies using ICT along the supply chain of manufacturing industries. 216 data were used, SPSS and AMOS were used for the analysis methods. Study results showed that ICT internal collaboration capabilities affect agility, one of RTE capabilities, but not visibility and prediction, and ICT operational flexibility capabilities have affected all three RTE capacities. And, RTE capabilities had a huge impact on supply chain performance as expected. In this paper, it has been found that the ICT capabilities in manufacturing are an important factor in improving RTE capabilities that are important in the supply chain and improving the performance of the supply chain.

편집이라는 작업은 1895년 이후 110년 동안 다듬어진 2차원 영상 문법의 기술적 요소이자 쇼트(컷)를 연결하는 연출의 핵심이었다. 그러나 2013년 <Windy Day>가 나오면서 영상 문법에 대한 새로운 담론을 요구하고 있다. <Windy Day>는 프레임이 없이 360도 감상이 가능하며 시선과 연동하여 캐릭터 행동과 쇼트의 길이가 달라지는 리얼타임 360 도 3D애니메이션으로서 VR콘텐츠의 출발점이 되었다. 본고에서는 이러한 <Windy Day>의 의의를 소개하고 그동안 VR콘텐츠를 시점과 서사구조, 프레임 등의 개념으로 분석한 것과 달리 ‘인터랙티브 편집’이라는 새로운 시각으로 분석하였다. ‘인터랙티브 편집’은 본 연구에서 처음 제안하는 개념으로서 실시간 3D애니메이션의 속성인 시점에 따라 상호작용으로 영상 쇼트의 연출, 쇼트의 길이와 연결 등 편집적 행위가 실시간으로 발생한다는 점에 집중한 것이다. <Windy Day>는 롱테이크 형식으로 하나의 쇼트만 존재하므로 편집의 의미에 맞는 분석을 위해 공간의 변경에 따른 ‘암묵적 쇼트’를 정의하였다. <Windy Day>에는 350도로 주관적 감상이 가능한 8개의 ‘암묵적 쇼트’가 있으며 각 쇼트를 자연스럽게 연결하기 위해 시점을 유도할 수 있는 캐릭터의 액션과 장면 전환 명령을 위한 공간 트리거를 발견할 수 있었다. 연구가 개발자에게는 다소 기초적인 내용일 수 있으나 영상산업 발전의 중요한 계기가 된 ‘영상 편집’을 인터랙티브 개념으로 정의하고 <Windy Day>를 소개함과 함께 내용을 분석하였다는 데 의의를 찾을 수 있다.

Salmonella is one of the most important bacterial pathogens responsible for many zoonotic food-related infectious diseases. Quantitative detection of the foodborne Salmonella contamination in various food sources is therefore critical for preventing the related disease outbreaks. In this study, we developed and evaluated a reliable real-time polymerase chain reaction (RT-PCR) assay to detect the Salmonella contamination quantitatively. The experimental results showed that our invA gene-specific quantitative RT-PCR (qRT-PCR) assay provides a strong correlation between the Cq values and the direct plate counts of Salmonella species in the artificially formulated samples. Further study may be necessary to identify more accurate correlation and equation that can apply to Salmonella spp.

The concentrations of volatile organic compounds (VOCs) and odor-inducing substances were measured using selected ion flow tube mass spectrometers (SIFT-MS) and a drone equipped with an air quality monitoring system. SIFT-MS can continuously measure the concentration of VOCs and odor-inducing substances in realtime without any pre-treating steps for the sample. The vehicle with SIFT-MS was used for real-time measurement of VOC concentration at the site boundaries of pollution sources. It is possible to directly analyze VOCs concentration generated at the outlets by capturing air from the pollution sources with a drone. VOCs concentrations of nine spots from Banwol National Industrial Complex were measured by a vehicle equipped with SIFT-MS and were compared with the background concentration measured inside the Metropolitan Air Quality Management Office. In three out of the nine spots, the concentration of toluene, xylene, hydrogen sulfide, and methyl ethyl ketone was shown to be much higher than the background concentration. The VOCs concentrations obtained using drones for high-concentration suspected areas showed similar tendencies as those measured using the vehicle with SIFTMS at the site boundary. We showed that if both the drone and real-time air quality monitoring equipment are used to measure VOCs concentration, it is possible to identify the pollutant sources at the industrial complex quickly and efficiently check sites with high concentrations of VOCs.

PURPOSES: This paper presents the development and evaluation of the smart hardware-in-the-loop systems (SMART-HILS) that evaluate traffic signal operations of a new real-time traffic signal control system called SMART SIGNAL at the traffic management center (TMC) level. METHODS: The layouts of the hardware and software components of the SMART-HILS were introduced in this study and its performance was tested using real-time traffic signal operation algorithms embedded in the SMART SIGNAL control server by utilizing the VISSIM simulation model. In this study, the SMART-HILS management software was developed using .NET programming language. Fewer random seed numbers were used for the test scenarios by conducting statistical tests to address the shortcomings of a longer time due to the adoption of the simulation time as the real-time by the TMC server. RESULTS : It was determined that SMART-HILS can communicate with TMC and VISSIM for both upload and download directions within acceptable time constraints and evaluate new design algorithms for traffic signal timing. CONCLUSIONS : In practice, traffic engineers can utilize SMART-HILS for testing the traffic signal operation alternatives before their selection and implementation. This application could increase the productivity of traffic signal operation.

In this study, real-time monitoring of air quality using a real-time mobile monitoring system was conducted to identify the emission characteristics and current status of air pollutants and odorous substances that are mainly generated in domestic dyeing industrial areas and to trace the pollutant sources. The concentration of toluene in the industrial area was detected up to 926.4 ppb, which was 3 to 4 times higher than that of other industrial areas. The concentration of methylethylketone was 124.7 ppb and the concentration of dichloromethane was 129.5 ppb. Acrolein concentration was highest at E point at 521.6 ppb, methanol concentration was highest at D point at 208.8 ppb, and acetone concentration was highest at M and N points at 549.3 ppb. The most frequently detected concentration of pollutants in the air quality monitoring results in the industrial area was, in descending order, toluene > methanol > acrolein > dichloromethane > acetone, which was similar to the chemical emissions used in the industrial area by the Pollutant Release and Transfer Register data. The concentration of odorous substances measured in real time was compared with the concentration of minimum detection, and the concentration of hydrogen sulfide was about 10 times higher than the concentration of minimum detection at A point, which was judged to be the main odorous cause of A point. In the future, if the real-time mobile measurement system is constructed to automatically connect wind direction/wind speed, PRTR (Pollutant Release and Transfer Register) data and SEMS (Stack Emission Management System) data, etc., it was judged that more accurate monitoring could be performed.