• Proceedings of the Korea Water Resources Association Conference
• /
• 2016.05a
• /
• pp.589-589
• /
• 2016

최근 도시화 및 기후변화에 의한 홍수피해의 증가로 인하여 이에 대응하는 방안으로 저영향 개발(LID) 요소기술에 관하여 다양하게 개발이 되고 있다. 하지만 이러한 요소기술에 대한 효율을 검증할 수 있는 표준화된 검증방법 및 기기는 부재한 실정이다. 본 연구에서는 LID 기법 및 개수로에서의 수리학적 실험 및 관측이 가능한 스마트 적응형-다목적 광폭 개수로 실험 장치를 개발하였다. 다목적 개수로를 이용한 실험으로는 수문학적 측면에서 첨두 유출량, 침투량, 지체 및 저류 효과, 차단, 유출빈도 등을 확인할 수 있으며 토질학적 측면에서는 비탈면 침식, 토양침식, 사면 안정 등을 실험할 수 있다. 또한 환경적 측면에서는 오염 부하량, 토양 유실량, 토사유출, 유사 이동, 비점오염원 등을 실험 할 수 있다.

• Korean Journal of Remote Sensing
• /
• v.37 no.1
• /
• pp.57-67
• /
• 2021

An earthquake occurred on 17 November 2017 in Pohang, South Korea with a strength of 5.4 Mw. This is the second strongest earthquake recorded by local authorities since the equipment was first installed. In order to improve understanding of earthquakes and surface deformation, many studies have been conducted according to these phenomena. In this research, we will estimate the surface deformation using the Okada model equation. The SAR images of three satellites with different wavelengths (ALOS-2, Cosmo SkyMed and Sentinel-1) were used to produce the interferogram pairs. The interferogram is used as a reference for surface deformation changes by using Okada to determine the source of surface deformation that occurs during an earthquake. The Non-linear optimization (Levemberg-Marquadrt algorithm) and Monte Carlo restart was applied to optimize the fault parameter on modeling process. Based on the modeling results of each satellite data, the fault geometry is ~6 km length, ~2 km width and ~5 km depth. The root mean square error values in the surface deformation model results for Sentinel, CSK and ALOS are 0.37 cm, 0.79 cm and 1.47 cm, respectively. Furthermore, the results of this modeling can be used as learning material in understanding about seismic activity to minimize the impacts that arise in the future.

• Journal of Drive and Control
• /
• v.17 no.4
• /
• pp.97-102
• /
• 2020

This paper presents the basic research for the design and fabrication of a 3D-printed load cell made of NCPC (nano-carbon piezo-resistive composite). We designed a structure that can resonate at a low frequency range of about 5-6 Hz with ANSYS using sensitivity analysis and a response surface method. The design was verified by fabricating the device with a low-quality commercial 3D printer and ABS filament. We conducted a feasibility test for a commercial sensor using 1000 cyclic load tests at 0.3 Hz in a material testing system. A manufacturing process for the 3D printer filament based on the NCPC was also developed using the nano-composite process.

• Journal of Drive and Control
• /
• v.19 no.4
• /
• pp.97-103
• /
• 2022

Low fertility rates and increased life expectancy further exacerbate the process of an aging society. This is also reflected in the gradual increase in the proportion of vulnerable groups in the social population. The demand for improved mobility among vulnerable groups such as the elderly or the disabled has greatly driven the growth of the electric-assisted mobility device market. However, such mobile devices generally require a certain operating capability, which limits the range of vulnerable groups who can use the device and increases the cost of learning. Therefore, autonomous driving technology needs to be introduced to make mobility easier for a wider range of vulnerable groups to meet their needs of work and leisure in different environments. This study uses mini PC Odyssey, Velodyne Lidar VLP-16, electronic device and Linux-based ROS program to realize the functions of working environment recognition, simultaneous localization, map generation and navigation of electric powered mobile devices for vulnerable groups. This autonomous driving mobility device is expected to be of great help to the vulnerable who lack the immediate response in dangerous situations.

• Smart Structures and Systems
• /
• v.29 no.4
• /
• pp.589-598
• /
• 2022

Timber structures are susceptible to structural damages caused by variations in moisture content (MC), inducing severe durability deterioration and safety issues. Therefore, it is of great significance to detect MC levels in timber structures. Compared to current methods for timber MC detection, which are time-consuming and require bulky equipment deployment, Lead Zirconate Titanate (PZT)-enabled stress wave sensing combined with statistic machine learning classification proposed in this paper show the advantage of the portable device and ease of operation. First, stress wave signals from different MC cases are excited and received by PZT sensors through active sensing. Subsequently, two non-baseline features are extracted from these stress wave signals. Finally, these features are fed to a statistic machine learning classifier (i.e., naïve Bayesian classification) to achieve MC detection of timber structures. Numerical simulations validate the feasibility of PZT-enabled sensing to perceive MC variations. Tests referring to five MC cases are conducted to verify the effectiveness of the proposed method. Results present high accuracy for timber MC detection, showing a great potential to conduct rapid and long-term monitoring of the MC level of timber structures in future field applications.

• International Journal of Internet, Broadcasting and Communication
• /
• v.14 no.3
• /
• pp.292-298
• /
• 2022

Coding education and algorithm education are essential in the era of the fourth industrial revolution. Text-oriented algorithm textbooks are perceived as difficult by students who are new to coding and algorithms. There is a need to develop educational content so that students can easily understand the principles of complex algorithms. This paper has implemented basic sorting algorithms as augmented reality contents for students who are new to algorithm education. To make it easier to understand the concept and principles of sorting algorithms, sorting data was expressed as a 3D box and the comparison of values according to the algorithms and the movement of values were produced as augmented reality contents in the form of 3D animations. In order to help with the understanding of sorting algorithms in C language, the change of variable values and the exchange of data were shown as animations according to the execution order of the code and the flow of the loop. Students can conveniently use contents through a smart phone without special equipment by being produced in a marker-based manner. Interest and immersion, as well as understanding of classes of sorting algorithms can be increased through educational augmented reality-based educational contents.

• Journal of Drive and Control
• /
• v.20 no.1
• /
• pp.34-40
• /
• 2023

The agricultural tractor ROPS test method according to OECD code 4 is a test to assess whether the driver's safety area can be secured when a tractor overturns, and reliability should be ensured. In this study, a model formula and procedure for calculating measurement uncertainty expressing reliability in the field of agricultural machinery testing were established according to the ISO/IEC Guide 98-3:2008. The characteristics of the ROPS test device were assessed and repeated tests were performed, and the were used as factors to calculate the measurement uncertainty. As a result of repeated tests, the accuracy was higher than 1.9 % in all load directions; thus, they were, applied to calculate the type A standard uncertainty. The final expanded uncertainty was calculated within the range of less than ± 7.76 kN of force and ± 6.96 mm of deformation in all load directions.

• Smart Structures and Systems
• /
• v.30 no.4
• /
• pp.385-395
• /
• 2022

Detecting cracks on a concrete structure is crucial for structural maintenance, a crack being an indicator of possible damage. Conventional crack detection methods which include visual inspection and non-destructive equipment, are typically limited to a small region and require time-consuming processes. Recently, to reduce the human intervention in the inspections, various researchers have sought computer vision-based crack analyses: One class is filter-based methods, which effectively transforms the image to detect crack edges. The other class is using deep-learning algorithms. For example, convolutional neural networks have shown high precision in identifying cracks in an image. However, when the objective is to classify not only the existence of crack but also the types of cracks, only a few studies have been reported, limiting their practical use. Thus, the presented study develops an image processing procedure that detects cracks and classifies crack types; whether the image contains a crazing-type, single crack, or multiple cracks. The properties and steps in the algorithm have been developed using field-obtained images. Subsequently, the algorithm is validated from additional 227 images obtained from an open database. For test datasets, the proposed algorithm showed accuracy of 92.8% in average. In summary, the developed algorithm can precisely classify crazing-type images, while some single crack images may misclassify into multiple cracks, yielding conservative results. As a result, the successful results of the presented study show potentials of using vision-based technologies for providing crack information with reduced human intervention.

• Korea Trade Review
• /
• v.47 no.2
• /
• pp.69-88
• /
• 2022

This paper aims to predict Busan's regional product and employment using the logistic regression models and machine learning models. The following are the main findings of the empirical analysis. First, the OLS regression model shows that the main industries such as electricity and electronics, machine and transport, and finance and insurance affect the Busan's income positively. Second, the binomial logistic regression models show that the Busan's strategic industries such as the future transport machinery, life-care, and smart marine industries contribute on the Busan's income in large order. Third, the multinomial logistic regression models show that the Korea's main industries such as the precise machinery, transport equipment, and machinery influence the Busan's economy positively. And Korea's exports and the depreciation can affect Busan's economy more positively at the higher employment level. Fourth, the voting ensemble model show the higher predictive power than artificial neural network model and support vector machine models. Furthermore, the gradient boosting model and the random forest show the higher predictive power than the voting model in large order.

• Journal of the Korean Society of Industry Convergence
• /
• v.27 no.3
• /
• pp.701-707
• /
• 2024

Industry 4.0 has brought about significant changes in the roles of workers through the introduction of innovative technologies. In smart factory environments, workers are required to interact seamlessly with robots and automated systems, often utilizing equipment enhanced by Virtual Reality (VR) and Augmented Reality (AR) technologies. This study aims to systematically analyze recent research literature on monitoring the physical and mental states of workers in Industry 4.0 environments. Relevant literature was collected using the Web of Science database, employing a comprehensive keyword search strategy involving terms related to Industry 4.0 and health monitoring. The initial search yielded 1,708 documents, which were refined to 923 journal articles. The analysis was conducted using VOSviewer, a tool for visualizing bibliometric data. The study identified general trends in the publication years, countries of authors, and research fields. Keywords were clustered into four main areas: 'Industry 4.0', 'Internet of Things', 'Machine Learning', and 'Monitoring'. The findings highlight that research on health monitoring of workers in Industry 4.0 is still emerging, with most studies focusing on using wearable devices to monitor mental and physical stress and risks. This study provides a foundational overview of the current state of research on health monitoring in Industry 4.0, emphasizing the need for continued exploration in this critical area to enhance worker well-being and productivity.