• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.50-61
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• 2022

Snow, rain, fog, and particulate matter interfere with the vehicle driver's vision, which causes a non-secure safety distance and an increase in speed deviation, causing repetitive large-scale traffic accidents. This study developed a road environment sensor capable of measuring 11 types of fog, snow, rain, temperature, humidity, direction of wind, speed of wind, Insolation, atmospheric pressure, fine particles, rainfall, etc. and compared the visibility measured by the infrared signal value of the development sensor. The relationship between the existing fog visibility sensor and the development sensor measurement was derived from data measured at a visibility of 500m or less that directly affects road safety.

• Journal of the Korean Association of Geographic Information Studies
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• v.26 no.4
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• pp.67-79
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• 2023

Service environments through laptops, smart devices, and various IoT devices are developing very rapidly. Recent security measures in these Internet environments mainly consist of network application level solutions such as firewall(Intrusion Prevention Systems) and IDS (intrusion detection system). In addition, various security data have recently been used on-site, and issues regarding the management and destruction of such security data have been raised. Products such as DRM(Digital Rights Management) and DLP(Data Loss Prevention) are being used to manage these security data. However despite these security measures, data security measures taken out to be used in the field are operated to the extent that the data is encrypted, delivered, and stored in many environments, and measures for encryption key management or data destruction are insufficient. Based on these issues we aim to propose a SecureOS Module, an OS-based security module. With this module users can manage and operate security data through a consistent interface, addressing the problems mentioned above.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.55-55
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• 2023

물 인프라 시설물은 다양한 산업 및 지역 사회에 필요한 물 공급과 이와 관련된 인프라를 제공하는 현대 사회의 중요한 구성 요소 중 하나이다. 이러한 시설이 노후화 되면서 안전과 신뢰성에 대한 우려가 커지면서 과거 건설, 개발 중심에서 유지, 관리 중심으로 패러다임이 변화하고 있다. 이에 물 인프라 시설물의 상태를 정밀하게 조사하고, 정확한 계측하는 기술에 대한 수요가 지속적으로 증가하고 있다. 최근, 드론에 초분광 센서를 탑재하여 초분광 영상을 수집할 수 있는 기술이 개발되고 있으며, 물 인프라 시설물에 대한 원격탐사 및 모니터링이 이러한 수요를 만족시킬 수 있는 유망한 해결책으로 부상하고 있다. 특히, 이러한 초분광 영상 수집 기술을 이용하면 물 인프라 시설물 주변의수심, 수질, 온도 등 환경적 요인 뿐만 아니라 재료에 따른 상태를 파악할 수 있어, 잠재적으로 구조 결함을 감지하는데 필요한 상세한 분광 정보를 수집할 수 있다. 또한, "저수지·댐의 안전관리 및 재해예방에 관한 법률"에서 정기적인 정밀안전진단을 요구하고 있으며, "중대재해처벌법"에 따라 인력중심의 조사, 계측 방식의 어려움이 있는 상황에서 드론 기반의 초분광 원격탐사 기술은 매력적인 선택지이다. 본 연구는 안전과 신뢰성에 대한 우려가 커지고 있는 물 인프라 시설물에서 드론 기반 초분광영상 기술이 제공하는 새로운 해결책에 대한 연구로, "저수지·댐 안전관리 및 재해예방에 관한 법률"에서 제시하는 물 인프라 시설의 정기적인 검사 및 유지보수에 대한 중요성을 더욱 강조하는 것으로, 물 인프라 시설물을 정확하게 모니터링하고 조사, 계측하는 능력의 중요성을 증가시킬 수 있는 기술이다. 따라서 본 연구에서는 드론 기반의 초분광 영상 수집 기술을 활용하여 물 인프라시설물의 원격탐사 및 모니터링에 대한 적용성을 검토하고자 한다. 이를 통해 드론 기반 초분광영상 기술이 물 인프라 시설물의 조사 및 계측, 유지 보수에 대한 새로운 해결책이 될 수 있는지 검토한다. 또한 이 기술의 잠재적인 이점과 한계를 탐구하고, 정확하고 신뢰성 높은 계측, 조사 기술에 대한 증가하는 수요를 충족시키기 위한 능력을 평가 하고자 한다.

• Smart Media Journal
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• v.9 no.1
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• pp.75-83
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• 2020

Compensation services that are indispensable during large-scale public utilities projects have been gradually increasing with the recent increase in construction, but there are no systematic compensation services due to the complicated procedures and manual work. For this reason, various problems such as construction period delays due to various complaints, corruption in compensation work, and impossible to trace the history of compensation data in the past are emerging. In this paper, in order to solve this problem, in-depth interviews and questionnaires were conducted to find out the problems of each compensation status. Based on this, 3 core technologies and 10 technical needs based on ICT were selected to improve the compensation work by deriving STEEP analysis and Issue Tree. The three core technologies are big data-based decision-making and prediction technology, advanced measurement technology, and open cloud-based compensation platform technology. In order to introduce the derived technologies to the institutions in charge of compensation, the possibility of technology diffusion by project operators was suggested based on the results of the current status of informatization by institution. Based on the core technology derived from this paper, it is necessary to make a prototype that can be advanced in compensation work and apply it to each institution and analyze the effect.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.3
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• pp.34-46
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• 2021

In order to accelerate the commercialization of self-driving cars, it is necessary to accurately identify the causes of deteriorating the driving safety of the current self-driving cars and try to improve them. This study conducted a questionnaire survey of experts studying autonomous driving in Korea to identify the causes of problems in the driving safety of autonomous vehicles and the level of autonomous driving technology in Korea. As a result of the survey, the construction section, heavy rain/heavy snow conditions, fine dust conditions, and the presence of potholes were less satisfied with the current technology level than their importance, and thus priority research and development was required. Among them, the failure of road/road facilities and the performance of the sensor itself in the construction section and the porthole, and the performance of the sensor and the absence of an algorithm were the most responsible for the situation connected to the weather. In order to realize safe autonomous driving as soon as possible, it is necessary to continuously identify and resolve the causes that hinder the driving safety of autonomous vehicles.

• Journal of the Korean Recycled Construction Resources Institute
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• v.10 no.3
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• pp.227-234
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• 2022

In this study, the rheological properties of cement paste composites applied with carbon-based nano-materials were experimental analyzed. Flow table and rheological properties, compressive strength were measured in the cement paste using graphene oxide asqueous solution and carbon nanotube aqueous solution. When carbon nano-materials was mixed in an aqueous solution, flow decreased and plastic viscosity and shear stress were increased. In particular, graphene oxide rapidly increased the plastic viscosity and shear stress. In the case of carbon nanotube aqueous solution, when less than 0.2 % was mixed, the increase rate was low compared to graphene oxide. This is because the specific surface area of graphene, which is in the form of a plate, is large. The compressive strength showed a small amount in strength increase when graphene mix, and CNT had a strength about 112 % of OPC. Carbon-based nanomaterials, is considered that CNT are suitable more to be used construction materials. However, extra studies on the surfactant to be used for mixing proportion and dispersion will be needed.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.3
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• pp.419-432
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• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.5
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• pp.711-721
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• 2022

Due to a long-term pandemic of COVID-19 since 2019, our society finally goes into 'un-tact (non-contact) era' in earnest by increasing an importance of non-contacted activities which are able to live without contacts among people. Un-tact era has influenced to generate a huge structural change with digitalization in our daily life as well as industrial society based on digital twin as an essential technology. As a technology representing real world in virtual digital world by integrating various technologies applied to 4^th industrial revolution, digital twin leads an innovation in industrial society with diverse industrial processes. But this technique isn't actively used across all industries due to structural limitations and environmental restrictions of digital twin. Especially, the field of construction and facility is necessary to use digital twin because it requires periodic managements of buildings in daily life and is directly connected with casualties in cases of disaster. But issues faced in reality are acting as an obstacle for applying digital twin. Therefore, this study suggests the necessity applying digital twin in facility safety response based on these issues and emphasizes active applications of digital twin by describing the expected effects being created. Furthermore, it connects to create demands of digital twin by overcoming current issues and suggesting high sustainable development.

• Journal of the Korea Institute of Building Construction
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• v.22 no.5
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• pp.473-483
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• 2022

BIM(Building Information Modeling) is a technology that can manage information throughout the entire life cycle of the construction industry and serves as a platform for improving productivity and integrating the entire construction industry. Currently, BIM is actively applied in developed countries, and its use at various overseas construction sites is increasing This is unclear. due to air shortening and budget savings. However, there is still a lack of institutional basis and technical limitations in the domestic construction sector, which have led to the lack of utilization of BIM. Various activation measures and institutional frameworks will need to be established for the early establishment of these productive BIMs in Korea. Therefore, as part of the research for the domestic settlement and revitalization of BIM, this study derived a number of key factors necessary for the development of the construction industry through brainstorming and expert surveys using AHP techniques and analyzed the relative importance of each factor. In addition, prior surveys by a group of experts resulted in 1, 3 items in level, 2, 9 items in level, and 3, 27 items in level, and priorities analysis was performed through pairwise comparisons. As a result of the AHP analysis, it was found that the relative importance weight of policy aspects was highest in level 1, and the policy factors in level 2 and the cost-based and incentive system introduction factors were considered most important in level 3. These findings show that the importance of the policy guidance or institutions underlying the activation of BIM rather than research and development or corporate innovation is relatively high, and that the preparation of policy plans by public institutions should be the first priority. Therefore, it is considered that the development of a policy system or guideline must be prioritized before it can be advanced to the next activation stage. The use of BIM technologies will not only contribute to improving the productivity of the construction industry, but also to the overall development of the industry and the growth of the construction industry. It is expected that the results of this study can provide as useful information when establishing policies for activating BIM in central government, relevant local governments, and related public institutions.

• Journal of the Society of Disaster Information
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• v.18 no.2
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• pp.269-279
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• 2022

Purpose: The purpose of this study is to develop a deep learning-based personal protective equipment detection model for disaster prevention at construction sites, and to apply it to actual construction sites and to analyze the results. Method: In the method of conducting this study, the dataset on the real environment was constructed and the developed personal protective equipment(PPE) detection model was applied. The PPE detection model mainly consists of worker detection and PPE classification model.The worker detection model uses a deep learning-based algorithm to build a dataset obtained from the actual field to learn and detect workers, and the PPE classification model applies the PPE detection algorithm learned from the worker detection area extracted from the work detection model. For verification of the proposed model, experimental results were derived from data obtained from three construction sites. Results: The application of the PPE recognition model to construction site brings up the problems related to mis-recognition and non-recognition. Conclusions: The analysis outcomes were produced to apply the object recognition technology to a construction site, and the need for follow-up research was suggested through representative cases of worker recognition and non-recognition, and mis-recognition of personal protective equipment.