• Korean Association of Arts Management
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• no.59
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• pp.73-99
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• 2021

Free admission policies have been gradually extended for the public to use cultural facilities free of charge, which lowered the barriers to use those facilities and has a great effect on increasing visitor demands. However, the annual number of visitors which is open to the public isn't quite accurate and varies from institution to institution, which means just a head count. Especially people counter overestimates the number of visitors because it counts visitors in duplicate. Therefore, the purpose of this study is to prepare effective way of grasping the number of visitors using cultural infrastructure. First, comparable number of visitors should be measured by defining the notion of visitor clearly, which has been measured vaguely. Secondly, the problem of duplicate count, which is considered the most problematic, should be solved. Thirdly, the various analysis of visitor behavior should be conducted to provide a high-quality service. To work out the problems above, new measurement will be presented here. This study suggests a state-of-the-art wireless signal-based measurement that could eliminate the duplicate data by collecting MAC address -smart device's distinct signal value. And it also could analyze diverse visitor behaviors by understanding a flow of visitor traffic, duration of stay and revisitation. I would like to examine the possibility and effectiveness of this new measurement by testing it.

• Korean Journal of Environment and Ecology
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• v.10 no.2
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• pp.211-239
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• 1997

The flora of Oesorak in the Soraksan National Park including Chombongsan(Mountain) and Kwanmobong(Peak) were surveyed from February to August, 1996. These areas have very rich and diverse flora;620 taxa with 89 families, 321 genera, 526 species, 2 subspecies, 89 varieties and 3 forms in the Oesorak, 404 taxa with 251 genera, 350 species, 1 subspecies, 51 varieties and 2 forms in the Chombongsan(Mountain), 286 taxa with 206 genera, 233 species, 1 subspecies, 50 varieties and 2 forms in Kwanmobong(peak). The Oesorak had very distinct floristic characteristics such as the wild habitats of Asarum maculatum(Aristolochiaceae) and Ilex macropoda(Aquifoliaceae). In the phyorgeographical point of view, the six species such as Sapium japonicum (Euphorbiaceae), Euphorbia joldini(Euphorbiaceae), Ilex macropoda (Aquifoliaceae), Styrax japonica (Styracaceae), Carex sideros ticta (Cyperaceae) and Asarum maculatum (Aristolochiaceae) were naturalized into this region, while the 17 taxa such as Abies neprolepis(Pinaceae), Pinus pumila(Pinaceae), Thuja koraiensis(Cupressaceae), Allium senescens(Liliaceae), Lilium distichum(Liliaceae), Saxifraga punctata(Saxfragaceae), Rosa marretii(Rosaceae), Bupleurum euphorbioides(Umbelliferae), Androsace cortusaefolia (Primulaceae), Peducularis mandshurica(Scrophulariaceae) and Leontopodium coreanum (Compositae) were distrivuted to this region. The colonizing weedy species such as Ixris repens (Compositae) were distributed to this region. The colonizing weedy species losa(Labiatae) and Rosa rugosa(Rosaceae) were naturalized into ca. 900m at sea level mainly due to the sand soil from the seashore. Mountain roadbed is susceptible than other areas to the slippery road problems, due largely to snow and rain, particularly during winter. Sand soils from seashore are utilized to minimize this slip in traffic operation.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2022.10a
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• pp.562-565
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• 2022

Recently, the increasing number of overloaded vehicles on the road poses a risk to traffic safety, such as falling objects, road damage, and chain collisions due to the abnormal weight distribution, and can cause great damage once an accident occurs. However, this irregular weight distribution is not possible to be recognized with the current weight measurement system for vehicles on roads. To address this limitation, we propose to build an object detection-based AI model to identify overloaded vehicles that cause such social problems. In addition, we present a simple yet effective method to construct an object detection model for the large-scale vehicle images. In particular, we utilize the large-scale of vehicle image sets provided by open AI-Hub, which include the overloaded vehicles from the CCTV, black box, and hand-held camera point of view. We inspected the specific features of sizes of vehicles and types of image sources, and pre-processed these images to train a deep learning-based object detection model. Finally, we demonstrated that the detection performance of the overloaded vehicle was improved by about 23% compared to the one using raw data. From the result, we believe that public big data can be utilized more efficiently and applied to the development of an object detection-based overloaded vehicle detection model.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.22 no.1
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• pp.192-207
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• 2023

Commercialization of level-4 (Lv.4) autonomous driving applications requires the definition of a safe road environment under which autonomous vehicles can operate safely. Thus, a risk assessment model is required to determine whether the operation of autonomous vehicles can provide safety to is sufficiently prepared for future real-life traffic problems. Although the risk factors of autonomous vehicles were selected and graded, the decision-making method was applied as qualitative data using a survey of experts in the field of autonomous driving due to the cause of the accident and difficulty in obtaining autonomous driving data. The fuzzy linguistic representation of decision-makers and the fuzzy analytic hierarchy process (AHP), which converts uncertainty into quantitative figures, were implemented to compensate for the AHP shortcomings of the multi-standard decision-making technique. Through the process of deriving the weights of the upper and lower attributes, the road alignment, which is a physical infrastructure, was analyzed as the most important risk factor in the operation risk of autonomous vehicles. In addition, the operation risk of autonomous vehicles was derived through the example of the risk of operating autonomous vehicles for the 5 areas to be evaluated.

• Journal of the Korean Geotechnical Society
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• v.26 no.3
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• pp.59-68
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• 2010

Systematic risk management is necessary in grand scaled urban construction because of the existence of complicated and various risk factors. Problems of obstructions, adjacent structures, safety, environment, traffic and geotechnical properties need to be solved because urban construction is progressed in limited space not as general earthwork. Therefore the establishment of special risk management system is necessary to manage not only geotechnical properties but also social and cultural uncertainties. This research presents the technique analysis by the current state of risk management technique. Risk factors were noticed and the importance of each factor was estimated through survey. The systemically categorized database was established. Risk extraction module, matrix and score module were developed based on the database. Expected construction budget and time distribution can be computed by Monte Carlo analysis of probabilities and influences. Construction budgets and time distributions of before and after response can be compared and analyzed 80 the risks are manageable for entire whole construction time. This system will be the foundation of standardization and integration. Procurement, efficiency improvement, effective time and resource management are available through integrated management technique development and application. Conclusively decrease in cost and time is expected by systemization of project management.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.6
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• pp.7-20
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• 2017

The advancement of cloud and big data and the considerable growth of traffic have increased the complexity and problems in the management inefficiency of existing networks. The software-defined networking (SDN) environment has been developed to solve this problem. SDN enables us to control network equipment through programming by separating the transmission and control functions of the equipment. Accordingly, several studies have been conducted to improve the performance of SDN controllers, such as the method of connecting existing legacy equipment with SDN, the packet management method for efficient data communication, and the method of distributing controller load in a centralized architecture. However, there is insufficient research on the control of SDN in terms of the quality of network-using applications. To support the establishment and change of the routing paths that meet the required network service quality, we require a mechanism to identify network requirements based on a contract for application network service quality and to collect information about the current network status and identify the violations of network service quality. This study proposes a method of identifying the quality violations of network paths through ontology to ensure the network service quality of applications and provide efficient services in an SDN environment.

• The Journal of Bigdata
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• v.6 no.2
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• pp.125-137
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• 2021

The number of CCTV installed in Korea is over 1.3 million, increasing by more than 15% annually. However, due to the limited budget compared to the installation demand, the infrastructure is composed of 500,000 pixel low-quality CCTV, and there is a limits on identification of objects in the video. Public CCTV has high utility in various fields such as crime prevention, traffic information collection (control), facility management, and fire prevention. Especially, since installed in high height, it works as its role in solving diverse crime and is in increasing trend. However, the current public CCTV field is operated with potential problems such as inability to identify due to environmental factors such as fog, snow, and rain, and the low-quality of collected images due to the installation of low-quality CCTV. Therefore, in this study, in order to remove the typical low-quality elements of public CCTV, the method of attenuating scattered light in the image caused by dust, water droplets, fog, etc and algorithm application method which uses deep-learning algorithm to improve input video into videos over quality over 4K are suggested.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.6A
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• pp.597-605
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• 2009

The safety of expressway bridges was estimated by checking the external condition rank based on the nondestructive inspection and material test and by measuring load carrying capacity based on the result of load test. Although the load carrying capacity of the bridges was clearly low compared to the design standard, it was examined that many of the bridges have good external condition rank relatively. Also, it can be assured that load carrying capacity shows a considerable difference according to various condition even though the bridges have similar construction year and a structural type. Therefore, this study showed various problems of the current safety measurement of expressway bridges by considering the status of the expressway bridges, external condition rank, and method of safety diagnosis and repair, rehabilitation for maintenance. Based on the existing data of over 400 expressway bridges, the load carrying capacity was analyzed quantitatively considering bridge type, serviced life, design live load, external condition rank and traffic count as variables. The result of this study will be expected to provide the basic information for a reasonable safety assessment of expressway bridge.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.

• Information Systems Review
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• v.25 no.4
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• pp.113-130
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• 2023

Due to economic development and increasing gross national income, the number of automobiles continues to rise, leading to a serious issue of illegal parking due to limited road conditions and insufficient parking facilities. Illegal parking causes significant inconvenience and displeasure to people and can even result in accidents and loss of lives. The severity of accidents and their consequences, related to the growing number of vehicles and illegal parking, is escalating, particularly in the metropolitan areas. Consequently, efforts are being made to address this problem as a cause of social issues and come up with measures to reduce illegal parking. In particular, half of the public complaints in the metropolitan area are related to illegal parking, and the highest physical and human damage occurs in Gyeonggi. Thus, this study aims to use machine learning techniques based on data related to illegal parking in Suwon city, Gyeonggi, to categorize regional characteristics and propose effective measures to crack down on illegal parking. Additionally, practical, social, policy, and legal measures to decrease illegal parking in the metropolitan area are suggested. This study has academic significance in that it solved the problem of illegal parking, which is mentioned as one of the social problems that cause traffic congestion, by classifying regional characteristics using K-prototype, a machine learning algorithm. Furthermore, the results of this study contribute to practical and social aspects by providing measures to decrease illegal parking in the metropolitan area.