The development of IOT technology and artificial intelligence technology is promoting the smartization of manufacturing system. In this study, data extracted from acceleration sensor and current sensor were obtained through experiments in the cutting process of SKD11, which is widely used as a material for special mold steel, and the amount of tool wear and product surface roughness were measured. SVR (Support Vector Regression) is applied to predict the roughness of the product surface in real time using the obtained data. SVR, a machine learning technique, is widely used for linear and non-linear prediction using the concept of kernel. In particular, by applying GSVQR (Generalized Support Vector Quantile Regression), overestimation, underestimation, and neutral estimation of product surface roughness are performed and compared. Furthermore, surface roughness is predicted using the linear kernel and the RBF kernel. In terms of accuracy, the results of the RBF kernel are better than those of the linear kernel. Since it is difficult to predict the amount of tool wear in real time, the product surface roughness is predicted with acceleration and current data excluding the amount of tool wear. In terms of accuracy, the results of excluding the amount of tool wear were not significantly different from those including the amount of tool wear.

For the efficient teaching and learning of Vietnamese, the researcher paid attention to Project Based Learning and tried to apply it to the class. The researcher analyzed prior studies of PBL classes, including English and other foreign languages, and applied the theory of PBL to Vietnamese language education subjects, designed PBL classes, and utilized them in classes. In addition, the process in which the learners perform tasks (student presentation, peer-faculty evaluation, evaluation opinion reflection process), the results, and the questionnaire survey on learners were analyzed. As a result, it was found that PBL methods could also be applied in Vietnamese classes. The learners reorganized the learning contents into his or her own knowledge in the relationship between the learner’s own thoughts, experiences, knowledge, and understanding by referring to the instructor’s teaching plan and lecture. In addition, it was possible to achieve more useful and viable knowledge by listening to other people’s opinions and thoughts about their own knowledge, understanding, and interpretation, and through correction and supplementation processes. Also noteworthy is that through the PBL class, the level of knowledge of each student increased rapidly.

수역 내 충돌 위험 식별은 항해의 안전을 위해 중요하다. 본 연구에서는 거리 요인을 기반으로 한 군집화 방법인 계층 클러스 터링을 포함하는 새로운 충돌 위험 평가 방법을 도입했으며, 주변의 선박이 많은 경우 실시간 데이터, 그룹 방법론 및 예비 평가를 사용하여 선박을 분류하고 충돌위험평가를 기반으로 평가하였다(HCAAP 처리라 부른다). 조우하는 선박들의 군집은 계층 프로그램에 의해 모아지고, 예비 평가와 결합되어 상대적으로 안전한 선박을 걸러내었다. 그런 다음, 각 군집 내에서 조우하는 선박 사이의 최근접점(DCPA) 및 최근접점까지의 도착시간(TCPA)까지의 시간을 계산하여 충돌위험지수(CRI)와의 관계를 비교하였다. 조우하는 선박들간의 군집에서 CRI와 DCPA 및 TCPA 수학적 관계는 음의 지수 함수로 구성되었다. 이러한 CRI로부터 운영자는 명시된 해역에서 항해하는 모든 선박의 안전성을 보다 쉽게 평가할 수 있으며, 프레임워크는 해상운송의 안전과 보안을 개선하고 인명 및 재산 손실을 줄일 수 있다. 본 연구에 서 제안된 프레임워크의 효과를 설명하기 위해 국내의 목포 연안 해역에서 실험 사례 연구를 수행하였다. 그 결과, 본 연구의 프레임워크가 각 군집 내에서 조우 선박 간의 충돌 위험 지수를 탐지하고 순위를 매기는 데 효과적이고 효율적이라는 것을 보여 주었으며, 추가연구를 위한 자동 위험 우선순위를 지정할 수 있게 해주었다.

A lot of sensor and control signals is generated by an industrial controller and related internet-of-things in discrete manufacturing system. The acquired signals are such records indicating whether several process operations have been correctly conducted or not in the system, therefore they are usually composed of binary numbers. For example, once a certain sensor turns on, the corresponding value is changed from 0 to 1, and it means the process is finished the previous operation and ready to conduct next operation. If an actuator starts to move, the corresponding value is changed from 0 to 1 and it indicates the corresponding operation is been conducting. Because traditional fault detection approaches are generally conducted with analog sensor signals and the signals show stationary during normal operation states, it is not simple to identify whether the manufacturing process works properly via conventional fault detection methods. However, digital control signals collected from a programmable logic controller continuously vary during normal process operation in order to show inherent sequence information which indicates the conducting operation tasks. Therefore, in this research, it is proposed to a recurrent neural network-based fault detection approach for considering sequential patterns in normal states of the manufacturing process. Using the constructed long short-term memory based fault detection, it is possible to predict the next control signals and detect faulty states by compared the predicted and real control signals in real-time. We validated and verified the proposed fault detection methods using digital control signals which are collected from a laser marking process, and the method provide good detection performance only using binary values.

본 논문은 2020년 2학기, 각 대학에서 시도하였던 “실시간 온라인 수업” 경험에 대한 설문을 진행한 결과를 토대로 작성되었다. 이는 추후 온라인 기반 수업이 어떠한 방향으로 나아가야 하는가를 진단하고, 한문 교육은 어떠한 점을 염두에 두는 것이 바람직한가를 살피기 위함이었다. 이를 위해 실시간 온라인 수업이 차지한 비중과 사용한 플랫폼을 파악하고, 그 개선점에 대해 설문 내용을 중심으로 분석을 시도하였다. 우선, “비중”에 있어서는 100% 비대면 수업 중 과반수 이상의 과목에서 실시간 온라인 수업을 진행하였음을 알 수 있었다. “플랫폼”의 경우, 인문계열에서는 “줌(ZOOM)” 플랫폼에 대한 사용이 압도적으로 높았으며, “1대 1 채팅” 및 “소회의실” 기능이 긍정적인 평가를 받고 있음을 확인하였다. 마지막으로 “개선점”으로 지적된 소통의 부재와 집중도 하락 문제를 보완할 수 있는 방안으로 구체적 한문과 수업 방안을 소개하였다. 제한된 인원으로부터의 설문 조사 내용에 기반하고 있는 본 연구 내용을 일반론으로 단정하기에는 한계가 있다. 그럼에도 불구하고 중복적으로 제기되고 있는 문제에 대해서는 대안을 고민해야 한다고 생각하여 본 연구를 진행하였다. 또한 한문수업에 특화된 구체적이고 다양한 모델에 대해서 간략하게 소개하였는데, 이에 대한 보다 구체적인 연구는 후고를 기약한다.

PURPOSES : The driver's ability to make a commitment has resulted in excessive force and a lack of commitment. To solve this problem, we are developing an algorithm that analyzes resolution in real-time by introducing IoT and informs drivers of the completion of compaction. METHODS : Real-time compaction was analyzed by installing accelerometers on the rollers. To evaluate the algorithms, we conducted an apparent density test. RESULTS : The algorithm data and apparent density test data showed similar trends. This means that the proposed algorithms are sufficiently reliable. However, a lack of data samples and the fact that only data prior to completion of the commitment were analyzed may indicate a lack of reliability. CONCLUSIONS : In subsequent studies, the number of samples will be increased and the data after completion of the commitment analyzed to increase reliability. Introducing a tachometer will prevent the TVL from falling sharply when the direction of the rollers' progress changes. In addition, it is also planned to upgrade the algorithms by researching cases in which the algorithms did not produce satisfactory results owing to problems such as temperature and speed.

The leading source of occupational fatalities is a portable ladder in Korea because it is widely used in industry as work platform. In order to reduce victims, it is necessary to establish preventive measures for the accidents caused by portable ladder. Therefore, this study statistically analyzed injury death by portable ladder for recent 10 years to investigate the accident characteristics. Next, to monitor wearing of safety helmet in real-time while working on a portable ladder, this study developed an object detection model based on the You Only Look Once(YOLO) architecture, which can accurately detect objects within a reasonable time. The model was trained on 6,023 images with/without ladders and safety helmets. The performance of the proposed detection model was 0.795 for F1 score and 0.843 for mean average precision. In addition, the proposed model processed at least 25 frames per second which make the model suitable for real-time application.

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.

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.