To keep an enterprise's competitiveness on the condition of the automatic manufacturing system such as FA, FMS and CIM, all the maintenance problems should be considered seriously In not only in production and maintenance but also in related industrial safety. As we analyze in the surveys the maintenance management of domestic enterprises and the causes of Industrial accident, there will be necessity of drawing up countermeasures for preventing industrial accidents and for ensuring expertise maintenance technologies. Based on these analyses, the safety information system, maintenance management information system, and the machinery condition diagnosis technique are studied by using of the knowledge-based system under the real-time computer-operating environment and using fuzzy linguistic variable. This computer system based knowledge-based diagnosis can easily provide not only the knowledge of expert system about deterioration phenomenon of industrial robots, but also the knowledge of relating safety and facility all the time. Therefore, it is expected to improve the efficiency of business processes in the production and safety when we use this system.

The evaluation of mental workload is measured by subjective ratings, physiological signals. It takes long time to analysis the measured signals and is very tedious and time-consumming work. Therefore, to evaluate the affect of workload effectively, real-time measurement system is required. In this paper, real-time mental workload measurement system using cardiac autonomic indiced which reflect well the mental workload was developed and evaluated. Analyzed indices were HR, IBI, Lorentz plot, CSI, CVI, and LF/HF ratio of heart rate variability. The system was applied to evaluate the affect of arithmetic task and showed good results. This system was consisted of ECG amplifier, A/D converter, and personal computer, and algorithm was implemented using LabVIEW.

오늘날 기업환경은 고객의 요구에 대응하여 적시에 효율적으로 상품과 서비스를 공급하고 전체비용의 대부분을 차지하는 물류와 재고비용을 줄여 기업의 이윤을 극대화하는 것으로 변화하고 있다. 예전에 기업위주의 시장형성에서 고객위주로 시장이 변화하고 있는 것이다. 이에 따라 기업은 고객의 요구에 능동적으로 대응하기 위하여 리드타임의 단축이 필수적인 요소가 되어가고 있다. 이러한 기업환경에 변화에 따른 대안으로 나온 것이 실시간 제조와 공급사슬망관리(Supply Chain Management : SCM)이다. 본 연구는 실시간 제조를 지원하기 위하여 제조업체와 협력사간의 물류에 있어 Goldratt의 Drunm-Buffer-Rope 스케줄링을 이용하여 물류창고라는 Buffer를 두어 협력사중 재고의 부족현상이 일어날 경우에는 공급체인상의 능력제약자원(Capacity Constraint Resource: CCR)을 찾아 능력제약자원이 되는 협력사에 대하여 생산을 독촉하고 물류창고에서는 부족재고에 대한 불출을 개시하여 재고의 완충을 도모하고자 한다. 제조업체와 협력사간의 정보를 실시간 감시시스템(Real-Time Monitoring System : RTMS)를 응용하여 웹상에서 공유하며, 실시간 거래가 가능토록하는 공급사슬망 구축에 관한 연구와 이를 프로그램 구현을 통해 실제적으로 시험해 보고자 한다.

인천항은 우리나라 제 2의 수,출입항임에도 불구하고 지리적, 자연적 특성상 갑문이라는 조수간만의 차를 극복하기 위한 시설과 매우 다양한 화물의 취급으로 만성적인 체선·체화의 문제를 안고 있다. 본 연구에서는 이러한 인천항을 대상으로 항만운영의 기본인 선석배정문제를 기존 연구를 바탕으로 실제 인천항 운영에 있어서 행해지고 있는 선석의 연속관리측면과 전문가의 경험으로 축적된 비공식적인 규칙을 보다 면밀히 조사하여, 공식적인 인천항 배정규칙과 더불어 개선된 실시간 의사결정지원시스템을 구축하고자 한다. 특히 동일 하역사의 이웃한 선석을 하나의 선석군으로 묶어 연속으로 선박을 접안하는 사항과 사용자의 임의수정사항과 동적인 상황을 시스템에 반영함에 있어서 사용자 인터페이스를 강화하여 구축, 개발하였다.

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.

본 연구의 목적은 결혼이민자 대상 한국어 온라인 수업 현황을 살펴보고 학습자들의 수업 만족도와 인식을 조사하여 앞 으로 교육 방안을 모색하는 데에 있다. 이를 위해 2년 동안 진행 된 다문화센터 한국어 수업을 조사하고, 학습자를 대상으로 1차 만족도 조사, 2차 면담 조사를 하며 수업 형태 변화에 대한 의견 을 모색하였다. 그 결과 학습자들의 매체 활용 능력은 우수하였고 온라인 수업 만족도도 높았다. 온라인 수업은 육아와 가정생활을 함께 할 수 있다는 것을 장점으로 꼽았다. 단점은 집중력 저하, 의사소통 부족 등이라고 하였다. 온라인과 대면 수업을 병행한다 면 온라인 수업은 ‘토픽, 귀화, 쓰기’ 등 이해 중심 수업, ‘회화, 발음, 문화 특강’ 등은 교실 대면 수업으로 진행하는 것이 효과적일 것이다.본 연구를 통해 결혼이민자의 한국어 온라인 수업 방향에 대한 논의가 이루어지기를 기대한다.

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).

이 본 연구의 목적은 코로나 19로 인해 수업의 공백과 생활의 어려움을 겪었던 평생교육 강사들이 경험한 온라인 수업의 어려움과 이슈를 분석하는데 있다. 이를 위해 충남 소재 G 평생교육기관 의 강사 5명을 기관으로부터 추천받아 면담을 실시하였다. 분석 결과, 수업 준비 과정은 강사 개개 인에게 맡겨져 있었기 때문에 강사가 스스로 학습자를 파악하고, 수업 환경을 조성하기 위해 노력한 것으로 나타났다. 수업 실행 과정에서는 강사가 학습자와 소통하고 수업의 몰입도를 높이기 위해 노력해온 것으로 나타났다. 특히, 온라인 수업 환경에서 강사의 긴장도가 높음에도 불구하고 학습자를 구경꾼으로 만들지 않기 위해 노력하였음을 확인하였다. 수업 평가 과정에서는 교육활동에 대한 피드백이 대면 수업에 비해 줄어든 것으로 나타났고, 평가를 통한 구체적인 환류 정보가 확보되지 않았음을 확인하였다. 이러한 분석 결과를 토대로 평생교육 거점 기관에서는 강사들이 온라인 수업에 적절한 형태로 수업을 설계할 수 있도록 지원해야 한다는 점, 강사의 온라인 수업 역량 강화를 위한 지원이 필요하다는 점, 온라인 환경에 적합한 평가 방법의 탐색과 적용이 필요하다는 점을 제안하였고, 개별 평생교육기관 차원에서는 원활한 수업 환경 조성 및 학습자들의 참여도 증진을 위한 사전 준비 지원이 필요함을 제시하였다.

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.