We have developed a near real-time flare alerting system which (1) downloads the latest GOES-l0 1-8 Å X-ray flux 1-min data by an automated ftp program and shell scripts, (2) produces a beep sound in a simple IDL widget program when the flux is larger than a critical value, and (3) makes it possible to do a wireless alerting by a set of portable transceivers. Thanks to the system, we have made successful Ha flare observations by the Solar Flare Telescope in Bohyunsan Optical Astronomy Observatory. This system is expected to be helpful for ground-based flare observers.

To predict the oil spill dispersion phenomena in the ocean, the oil spill response model, which can be used for strategic purpose on the oil spill site, based on Lagrangian particle-tracking method was formulated and applied to the neighboring area with Pusan port where the oil spill incident occurred when the tanker ship No.1 Youil struck on a small rock near the Namhyungjeto on September 21, 1995. The real-time tidal currents to be required as input data of the oil spill model were obtained by the two-dimensional hydrodynamic model and the tide prediction model. Evaluation of tidal currents using observation data was successful. For wind data, other input data of oil spill model, observed data on the spot were used. To verify the oil spill model, the oil spill modelling results were compared with the field data obtained from the spill site. Compared the modelling results with the observation data, there exist some discrepancies but the general pattern of modelling results was similar to that of field observation. The modelling results on 7 days after spill occurred showed that the 40% of spilled oil is in floating, 36% in evaporated, 23% at shore, and 1% in out of boundary, respectively. According to the evaluation of weighting curves of effective components to the dispersion of oil, the winds make a 37% of contribution to the dispersion of oil, turbulent diffusion 39.5%, and tidal currents 23.5%, respectively. Provided the more accurate wind data are supported, more favorable results might be obtained.

This paper deals with desktop simulator which can be utilized for total assessment of safety in harbour manoeuvre on a personal computer. Current, wind and tug forces are treated as well as precise mathematical model of a ship for low advance speed including stopping behaviour with effect of water depth. By using the desktop simulator, the real time simulation of berthing and unberthing manoeuvres was applied to Pusan harbour as an example. It is concluded that the above simulation technique will be beneficial to assessment of ship's safety under the environmental conditions is harbour area.

We developed a real-time CCD photometric program for IBM PC compatibles. This program can perform several functions necessary for CCD photometry such as telescope control. filter rotation, CCD camera control and data acquisition, two-dimensional image processing, simple aperture photometry and automatic differential photometry. The image processing of a spiral galaxy M51 observed with the SNU 61cm reflector shows a well developed spiral structure. Simple aperture photometric results of 14 stars in open cluster NGC 6871 almost accorded with those from IRAF/APPHOT. And we could confirm the light variation of eclipsing binary EM Cep by simultaneous automatic differential photometry of 7stars in NGC 7160. Due to the excellence in the precision and efficiency of automatic differential photometry using CCD. this program is very useful for the study of small amplitude δ δ Sct type variables and of the photometric stability of B ∼ ∼ F type stars.

본 연구는 실시간 온라인으로 진행되는 학부 교양 초급 한국 어 수업에서 강의식 수업과 플립 러닝식 수업을 실시하고 각 수업 방 식에 대한 학습자 인식 및 수업 효율성을 살펴보는 데 목적이 있다. 이를 위해 ZOOM에서 8주간 각 수업 방식을 적용한 한국어 수업을 진행하고 사후 설문 조사를 실시하였다. 그 결과 학습자들은 강의식 수업보다는 플립 러닝식 수업에 긍정적인 반응을 보였으며, 강의식 수 업에서는 ‘어휘 및 문법’ 영역, 플립 러닝식 수업에서는 ‘듣기, 읽기, 쓰 기, 말하기’ 영역의 수업 효율성이 높게 나타났다. 이에 실시간 온라인 으로 진행되는 초급 한국어 수업의 효율을 높이기 위해서는 수업 환 경과 언어 영역의 특성을 고려한 혼합된 수업 방식이 필요할 것이다.

In this paper, among the W-S-R relationship methods proposed by Lee, et. al., (2020) to produce rain-based rain information in real time, we tried to produce actual rain information by applying machine learning techniques to take into account the effects of wiper operation. To this end, rain sensor proposed the Graded Descent and Threshold Map method for pre-processing the cumulative value of the difference before and after wiper operation by utilizing four sensitive channels for optical sensors developed by Kim Byung-sik (2016) and using rain sensor data produced by five rain conditions in indoor artificial rainfall experiments. This method is the method of producing rainfall information by calculating the average value of the Threshold according to the rainfall conditions and channels, creating a Threshold Map corresponding to the grid 4 (channel) x 5 (thinking of rainfall information) and applying Optima Rainfall Intensity among the big data processing techniques. For the verification of these proposed findings, the application was evaluated by comparing the rainfall observations with the methods presented by Lee, et. al., (2020).

Concern about water quality and pollution was heightened as contaminants flowed into the Namhan River downstream of the Chungju Dam. Thus, this study calculated the Real-Time Water Quality Index (RTWQI) using monthly average water quality monitoring data for major tributaries that require water quality control, which were obtained between 2010 and 2019. To review the applicability of the RTWQI for water quality assessment in the major tributaries of the Namhan River, it was compared with the living environment standard river. The calculated RTWQI result indicated that Bokhacheon, with 59 points, was in the “fair” grade, and this stream was identified as a tributary that preferentially requires water quality improvement as it showed a decreasing (-) tendency of RTWQI. A comparison between RTWQI and the living environment standard of T-P showed that the categories of “completely coincident” and “different by one grade” accounted for 45% of the total, and the water quality of major tributaries was assessed as low. Therefore, the RTWQI, which applies comprehensive water quality parameters and has higher reliability than assessing a single water quality parameter, was determined to be efficient for water quality assessment of the major tributaries of the Namhan River.

This research is a case study of underwater object tracking based on real-time recurrent regression networks (Re3). Re3 has the concept of generic object tracking. Because of these characteristics, it is very effective to apply this model to unclear underwater sonar images. The model also an pursues object tracking method, thus it solves the problem of calculating load that may be limited when object detection models are used, unlike the tracking models. The model is also highly intuitive, so it has excellent continuity of tracking even if the object being tracked temporarily becomes partially occluded or faded. There are 4 types of the dataset using multi-beam sonar images: including (a) dummy object floated at the testbed; (b) dummy object settled at the bottom of the sea; (c) tire object settled at the bottom of the testbed; (d) multi-objects settled at the bottom of the testbed. For this study, the experiments were conducted to obtain underwater sonar images from the sea and underwater testbed, and the validity of using noisy underwater sonar images was tested to be able to track objects robustly.

This paper proposes a model and train method that can real-time detect objects and distances estimation based on a monocular camera by applying deep learning. It used YOLOv2 model which is applied to autonomous or robot due to the fast image processing speed. We have changed and learned the loss function so that the YOLOv2 model can detect objects and distances at the same time. The YOLOv2 loss function added a term for learning bounding box values x, y, w, h, and distance values z as 클래스ification losses. In addition, the learning was carried out by multiplying the distance term with parameters for the balance of learning. we trained the model location, recognition by camera and distance data measured by lidar so that we enable the model to estimate distance and objects from a monocular camera, even when the vehicle is going up or down hill. To evaluate the performance of object detection and distance estimation, MAP (Mean Average Precision) and Adjust R square were used and performance was compared with previous research papers. In addition, we compared the original YOLOv2 model FPS (Frame Per Second) for speed measurement with FPS of our model.

Due to advances in machine intelligence and increased demands for autonomous machines, the complexity of the underlying software platform is increasing at a rapid pace, overwhelming the developers with implementation details. We attempt to ease the burden that falls onto the developers by creating a graphical programming framework we named Splash. Splash is designed to provide an effective programming abstraction for autonomous machines that require stream processing. It also enables programmers to specify genuine, end-to-end timing constraints, which the Splash framework automatically monitors for violation. By utilizing the timing constraints, Splash provides three key language semantics: timing semantics, in-order delivery semantics, and rate-controlled data-driven stream processing semantics. These three semantics together collectively serve as a conceptual tool that can hide low-level details from programmers, allowing developers to focus on the main logic of their applications. In this paper, we introduce the three-language semantics in detail and explain their function in association with Splash’s language constructs. Furthermore, we present the internal workings of the Splash programming framework and validate its effectiveness via a lane keeping assist system.

A new algorithm has been propose to detect the reflected light region as disturbances in a real-time vision system. There have been several attempts to detect existing reflected light region. The conventional mathematical approach requires a lot of complex processes so that it is not suitable for a real-time vision system. On the other hand, when a simple detection process has been applied, the reflected light region can not be detected accurately. Therefore, in order to detect reflected light region for a real-time vision system, the detection process requires a new algorithm that is as simple and accurate as possible. In order to extract the reflected light, the proposed algorithm has been adopted several filter equations and clustering processes in the HSI (Hue Saturation Intensity) color space. Also the proposed algorithm used the pre-defined reflected light data generated through the clustering processes to make the algorithm simple. To demonstrate the effectiveness of the proposed algorithm, several images with the reflected region have been used and the reflected regions are detected successfully.

The global trends of shorter delivery times and the safety of important payload in production networks are leading to higher synchronization efforts between production and delivery processes. By now, research activities in intelligent shipment are expanding quickly in the case of possibilities and importance of usage, which means payload that can identify, monitor or locate itself. In this study, it is proposed that new generation system for continuously monitoring payloads during delivery; real-time monitoring of truck loading states; a new improved algorithm for intelligent monitoring of delivery processing; the possibility of a detailed analysis of the truck loading states in real-time and payload safety; and more efficient truck tracking.

진동수 기반 내하력 평가모델을 실교량에 접목시켜 실시간으로 그 결과를 추정하기 위해 모니터링 및 평가 시스템 프레임 웍을 제시하고자한다. 실시간 내하력 평가 시스템은 무선 IOT가속도계를 부착하여 유효 상시진동데이터를 원격으로 획득하고 이를 통해 진동수 및 점성비를 분석하여 모델에서 요구하는 성능계수를 결정하고 이 결과들을 종합하여 이전 내하력 결과 대비 현재의 내하력을 추정하는 방법이다.

Collecting a rich but meaningful training data plays a key role in machine learning and deep learning researches for a self-driving vehicle. This paper introduces a detailed overview of existing open-source simulators which could be used for training self-driving vehicles. After reviewing the simulators, we propose a new effective approach to make a synthetic autonomous vehicle simulation platform suitable for learning and training artificial intelligence algorithms. Specially, we develop a synthetic simulator with various realistic situations and weather conditions which make the autonomous shuttle to learn more realistic situations and handle some unexpected events. The virtual environment is the mimics of the activity of a genuine shuttle vehicle on a physical world. Instead of doing the whole experiment of training in the real physical world, scenarios in 3D virtual worlds are made to calculate the parameters and training the model. From the simulator, the user can obtain data for the various situation and utilize it for the training purpose. Flexible options are available to choose sensors, monitor the output and implement any autonomous driving algorithm. Finally, we verify the effectiveness of the developed simulator by implementing an end-to-end CNN algorithm for training a self-driving shuttle.

하천수사용 관리를 적정하게 관리하기 위해서는 객관적인 하천수사용량 자료가 수집되어야 한다. 그러나, 하천수 사용자의 취수여건을 고려하여 유량계 외에 수문조작 등의 간접적인 계측방법을 인정하고 있고, 사용자의 자발적인 보고에 의존함에 따라 자료의 객관화가 어려운 실정이다. 이에 한강홍수통제소에서는 농업용수 사용 비중이 커 하천유량 파악이 어려운 만경강의 고산~봉동 수위관측소 구간에 위치한 어우보 취수로에 V-ADCP를 이용한 계측시설을 설치 및 운영하고 있다. 본 연구에서는 V-ADCP로 측정된 유속을 이용하여 실시간 하천수사용량 산정을 위한 유속분포법의 적 용성을 평가하였다. 이를 위해 Chiu의 2차원 유속분포식의 매개변수 민감도를 분석하고, 실측유량 자료에 기초한 최적 매개변수를 산정하였다. 또한 수위-유량관계법, 지표유속법과 비교 평가하여 유속분포법의 특성을 분석하였다.

In the midst of disaster, such as an earthquake or a nuclear radiation exposure area, there are huge risks to send human crews. Many robotic researchers have studied to send UGVs in order to replace human crews at dangerous environments. So far, two-dimensional camera information has been widely used for teleoperation of UGVs. Recently, three-dimensional information based teleoperations are attempted to compensate the limitations of camera information based teleoperation. In this paper, the 3D map information of indoor and outdoor environments reconstructed in real-time is utilized in the UGV teleoperation. Further, we apply the LTE communication technology to endure the stability of the teleoperation even under the deteriorate environment. The proposed teleoperation system is performed at explosive disposal missions and their feasibilities could be verified through completion of that missions using the UGV with the Explosive Ordnance Disposal (EOD) team of Busan Port Security Corporation.

Background : Development of new real-time PCR diagnosis method for simultaneous diagnosis of Cylindrocarpon destructans and Fusarium solani, causative fungi of ginseng root rot disease. C. destructans and F. solani are known to be the major pathogens of ginseng root rot disease. Root rot caused by these pathogens is a disease that is difficult to control because the disease progresses slowly and it is difficult to diagnose early and even when symptoms of plant seeding are present, the disease is already spread in the roots. Diagnostic methods to detect the presence or absence of ginseng roots rot fungi in soil before ginseng cultivation are currently being used as a method for controlling. However, commercialized soil extraction kits and PCR diagnostics have cost, diagnostic time, and single diagnostic problems, and need to develop new diagnostic methods. Methods and Results : Primers and probes in the beta-tubulin 2 gene were designed for species-specific detection. In silico analysis, the detection rate of C. destructans was 100% and the detection rate of F. solani was 95%. The multiplex real time PCR optimization conditions including the internal control were established. The analytical sensitivity using positive samples was 10 copies/㎕ for C. destructans and 10 copies/㎕ for F. solani. As a result of performance comparison test with conventional PCR diagnosis methods, it was confirmed that the developed multiplex real time PCR method has the same or better performance in terms of sensitivity. In the developed soil extraction kit, the extraction time was reduced and the extracted DNA quality was improved, compared to the used soil extraction kit. Conclusion : From the above results, we expect that the developed C. destructans / F. solani multiplex real time PCR diagnosis method and soil extraction kit will be useful for real-time monitoring of ginseng root rot pathogenic fungi in the soil of ginseng cultivation area and diagnosis of suitability of ginseng cultivation area.

현재 많은 증강현실 콘텐츠가 존재하지만 두 개 이상의 단말기가 실시간으로 통신하며 동시에 이용하는 증강현실 콘텐츠는 거의 상용화되지 않았다. 본 논문에서는 블루투스 통신을 이용하여 네트워크 증강현실 콘텐츠의 가능성에 대해 연구하려 한다. 이 연구를 통해 네트워크 증강현실 개발과 콘텐츠의 종류를 한층 더 넓힐 수 있을 것이다.

In this paper, real-time damage assessment technology was developed for detection the damage of bridges in real time and the performance of the developed technology was verified by vibration test. Real-time damage assessment technology was developed by combining statistical pattern recognition technology and simulation technology. In order to verify the developed technology, the earthquake response acquisition experiment was conducted according to the cable damage of the model cable-stayed bridge. As a result, it was confirmed that the developed real-time damage assessment technology can provide information on the location of damaged cable.