• Title/Summary/Keyword: 시스템 모델링

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A Study on Building Object Change Detection using Spatial Information - Building DB based on Road Name Address - (기구축 공간정보를 활용한 건물객체 변화 탐지 연구 - 도로명주소건물DB 중심으로 -)

  • Lee, Insu;Yeon, Sunghyun;Jeong, Hohyun
    • Journal of Cadastre & Land InformatiX
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    • v.52 no.1
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    • pp.105-118
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    • 2022
  • The demand for information related to 3D spatial objects model in metaverse, smart cities, digital twins, autonomous vehicles, urban air mobility will be increased. 3D model construction for spatial objects is possible with various equipments such as satellite-, aerial-, ground platforms and technologies such as modeling, artificial intelligence, image matching. However, it is not easy to quickly detect and convert spatial objects that need updating. In this study, based on spatial information (features) and attributes, using matching elements such as address code, number of floors, building name, and area, the converged building DB and the detected building DB are constructed. Both to support above and to verify the suitability of object selection that needs to be updated, one system prototype was developed. When constructing the converged building DB, the convergence of spatial information and attributes was impossible or failed in some buildings, and the matching rate was low at about 80%. It is believed that this is due to omitting of attributes about many building objects, especially in the pilot test area. This system prototype will support the establishment of an efficient drone shooting plan for the rapid update of 3D spatial objects, thereby preventing duplication and unnecessary construction of spatial objects, thereby greatly contributing to object improvement and cost reduction.

Steel Frame Clamp Deformation and Performance Check based on Clamping Orientation (철골용 클램프 시공방향에 따른 변형 및 성능확인)

  • Mo, Seung-Un;Lim, Nam-Gi
    • Journal of the Korea Institute of Building Construction
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    • v.22 no.2
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    • pp.161-169
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    • 2022
  • The government [3] specifies steel pipe scaffolding as conventional scaffolding and is promoting the installation of system scaffolding, an integrated work platform, and avoidance of the use of steel pipe scaffolding as much as possible. However, in places where equipment cannot enter, such as power plants and plant sites, places the structure is complex, and places where scaffolding cannot be stacked on the ground, there is no choice but to install steel pipe scaffolding. When installing steel pipe scaffolding on an H-beam structure at a high place, the performance of the steel frame clamp is very important in order to form a work space which workers can work safely. In this study, the displacement magnitude and tensile load of members in each installation direction of the clamp for steel frame were verified through performance tests and structural analysis modeling. As a result, it was confirmed that the performance for each installation direction satisfies the safety certification standard tensile load of 10,000N. Although the performance value is satisfactory, deformation of the attachment pressing bolt was verified and was confirmed to have minimal deformation. Thus, to ensure the load is properly to the attachment body, the clamp for a steel frame must be installed in the direction in which the load is transmitted.

Development of Data Analysis and Interpretation Methods for a Hybrid-type Unmanned Aircraft Electromagnetic System (하이브리드형 무인 항공 전자탐사시스템 자료의 분석 및 해석기술 개발)

  • Kim, Young Su;Kang, Hyeonwoo;Bang, Minkyu;Seol, Soon Jee;Kim, Bona
    • Geophysics and Geophysical Exploration
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    • v.25 no.1
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    • pp.26-37
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    • 2022
  • Recently, multiple methods using small aircraft for geophysical exploration have been suggested as a result of the development of information and communication technology. In this study, we introduce the hybrid unmanned aircraft electromagnetic system of the Korea Institute of Geosciences and Mineral resources, which is under development. Additionally, data processing and interpretation methods are suggested via the analysis of datasets obtained using the system under development to verify the system. Because the system uses a three-component receiver hanging from a drone, the effects of rotation on the obtained data are significant and were therefore corrected using a rotation matrix. During the survey, the heights of the source and the receiver and their offsets vary in real time and the measured data are contaminated with noise. The noise makes it difficult to interpret the data using the conventional method. Therefore, we developed a recurrent neural network (RNN) model to enable rapid predictions of the apparent resistivity using magnetic field data. Field data noise is included in the training datasets of the RNN model to improve its performance on noise-contaminated field data. Compared with the results of the electrical resistivity survey, the trained RNN model predicted similar apparent resistivities for the test field dataset.

Analysis of Remote Driving Simulation Performance for Low-speed Mobile Robot under V2N Network Delay Environment (V2N 네트워크 지연 환경에서 저속 이동 로봇 원격주행 모의실험을 통한 성능 분석)

  • Song, Yooseung;Min, Kyoung-wook;Choi, Jeong Dan
    • The Journal of The Korea Institute of Intelligent Transport Systems
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    • v.21 no.3
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    • pp.18-29
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    • 2022
  • Recently, cooperative intelligent transport systems (C-ITS) testbeds have been deployed in great numbers, and advanced autonomous driving research using V2X communication technology has been conducted actively worldwide. In particular, the broadcasting services in their beginning days, giving warning messages, basic safety messages, traffic information, etc., gradually developed into advanced network services, such as platooning, remote driving, and sensor sharing, that need to perform real-time. In addition, technologies improving these advanced network services' throughput and latency are being developed on many fronts to support these services. Notably, this research analyzed the network latency requirements of the advanced network services to develop a remote driving service for the droid type low-speed robot based on the 3GPP C-V2X communication technology. Subsequently, this remote driving service's performance was evaluated using system modeling (that included the operator behavior) and simulation. This evaluation showed that a respective core and access network latency of less than 30 ms was required to meet more than 90 % of the remote driving service's performance requirements under the given test conditions.

Development of Simulator for Analyzing Intercept Performance of Surface-to-air Missile (지대공미사일 요격 성능 분석 시뮬레이터 개발)

  • Kim, Ki-Hwan;Seo, Yoon-Ho
    • Journal of the Korea Society for Simulation
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    • v.19 no.1
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    • pp.63-71
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    • 2010
  • In modern war, Intercept Performance of SAM(Surface to Air Missile) is gaining importance as range and precision of Missile and Guided Weapon on information warfare have been improved. An aerial defence system using Surface-to-air Radar and Guided Missile is needed to be built for prediction and defense from threatening aerial attack. When developing SAM, M&S is used to free from a time limit and a space restriction. M&S is widely applied to education, training, and design of newest Weapon System. This study was conducted to develop simulator for evaluation of Intercept Performance of SAM. In this study, architecture of Intercept Performance of SAM analysis simulator for estimation of Intercept Performance of various SAM was suggested and developed. The developed Intercept Performance of SAM analysis simulator was developed by C++ and Direct3D, and through 3D visualization using the Direct3D, it shows procedures of the simulation on a user animation window. Information about design and operation of Fighting model is entered through input window of the simulator, and simulation engine consisted of Object Manager, Operation Manager, and Integrated Manager conducts modeling and simulation automatically using the information, so the simulator gives user feedback in a short time.

A Study on Improvement of Air Quality Dispersion Model Application Method in Environmental Impact Assessment (II) - Focusing on AERMOD Model Application Method - (환경영향평가에서의 대기질 확산모델 적용방법 개선 연구(II) - AERMOD 모델 적용방법을 중심으로 -)

  • Suhyang Kim;Sunhwan Park;Hyunsoo Joo;Minseop So;Naehyun Lee
    • Journal of Environmental Impact Assessment
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    • v.32 no.4
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    • pp.203-213
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    • 2023
  • The AERMOD model was the most used, accounting for 89.0%, based on the analysis of the environmental impact assessment reports published in the Environmental Impact Assessment Information Support System (EIASS) between 2021 and 2022. The mismatch of versions between AERMET and AERMOD was found to be 25.3%. There was the operational time discrepancy of 50.6% from industrial complexes, urban development projects between used in the model and applied in estimating pollutant emissions. The results of applying various versions of the AERMET and AERMOD models to both area sources and point sources in both simple and complex terrain in the Gunsan area showed similar values after AERMOD version 12 (15181). Emissions are assessed as 24-hour operation, and the predicted concentration in both simple and complex terrain when using the variable emission coefficient option that applies an 8-hour daytime operation in the model is lowered by 37.42% ~ 74.27% for area sources and by 32.06% ~ 54.45% for point sources. Therefore, to prevent the error in using the variable emission coefficient, it is required to clearly present the emission calculation process and provide a detailed explanation of the composition of modeling input data in the environmental impact assessment reports. Also, thorough reviews by special institutions are essential.

A Study of Pre-Service Secondary Science Teacher's Conceptual Understanding on Carbon Neutral: Focused on Eye Tracking System (탄소중립에 관한 중등 과학 예비교사들의 개념 이해 연구 : 시선추적시스템을 중심으로)

  • Younjeong Heo;Shin Han;Hyoungbum Kim
    • Journal of the Korean Society of Earth Science Education
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    • v.16 no.2
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    • pp.261-275
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    • 2023
  • The purpose of this study was to analyze the conceptual understanding of carbon neutrality among secondary school science pre-service teachers, as well as to identify gaze patterns in visual materials. For this study, gaze tracking data of 20 pre-service secondary school science teachers were analyzed. Through this, the levels of conceptual understanding of carbon neutrality were categorized for the participants, and differences in gaze patterns were analyzed based on the degree of conceptual understanding of carbon neutrality. The research findings are as follows. First, as a result of performing modeling activities to predict carbon emissions and removals until 2100 using the concept of '2050 carbon neutrality,' 50% of the participants held a conception that carbon emissions would continue to increase. Additionally, 25% of the participants did not properly understand the causal relationship between net carbon dioxide emissions and cumulative concentrations. Second, the gaze movements of the participants regarding visual materials related to carbon neutrality were significantly influenced by the information presented in the text area, and in the case of graphs, the focus was mainly on the data area. Moreover, when visual data with the same function and category were arranged, participants showed the most interest in materials explaining concepts or visual data placed on the left side. This implies a preference for specific positions or orders. Participants with lower levels of conceptual understanding and inadequate grasp of causal relationships among elements exhibited notably reduced concentration and overall gaze flow. These findings suggest that conceptual understanding of carbon neutrality including climate change and natural disaster significantly influences interest in and engagement with visual materials.

Analysis of articles on water quality accidents in the water distribution networks using big data topic modelling and sentiment analysis (빅데이터 토픽모델링과 감성분석을 활용한 물공급과정에서의 수질사고 기사 분석)

  • Hong, Sung-Jin;Yoo, Do-Guen
    • Journal of Korea Water Resources Association
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    • v.55 no.spc1
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    • pp.1235-1249
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    • 2022
  • This study applied the web crawling technique for extracting big data news on water quality accidents in the water supply system and presented the algorithm in a procedural way to obtain accurate water quality accident news. In addition, in the case of a large-scale water quality accident, development patterns such as accident recognition, accident spread, accident response, and accident resolution appear according to the occurrence of an accident. That is, the analysis of the development of water quality accidents through key keywords and sentiment analysis for each stage was carried out in detail based on case studies, and the meanings were analyzed and derived. The proposed methodology was applied to the larval accident period of Incheon Metropolitan City in 2020 and analyzed. As a result, in a situation where the disclosure of information that directly affects consumers, such as water quality accidents, is restricted, the tone of news articles and media reports about water quality accidents with long-term damage in the event of an accident and the degree of consumer pride clearly change over time. could check This suggests the need to prepare consumer-centered policies to increase consumer positivity, although rapid restoration of facilities is very important for the development of water quality accidents from the supplier's point of view.

BIM Mesh Optimization Algorithm Using K-Nearest Neighbors for Augmented Reality Visualization (증강현실 시각화를 위해 K-최근접 이웃을 사용한 BIM 메쉬 경량화 알고리즘)

  • Pa, Pa Win Aung;Lee, Donghwan;Park, Jooyoung;Cho, Mingeon;Park, Seunghee
    • KSCE Journal of Civil and Environmental Engineering Research
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    • v.42 no.2
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    • pp.249-256
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    • 2022
  • Various studies are being actively conducted to show that the real-time visualization technology that combines BIM (Building Information Modeling) and AR (Augmented Reality) helps to increase construction management decision-making and processing efficiency. However, when large-capacity BIM data is projected into AR, there are various limitations such as data transmission and connection problems and the image cut-off issue. To improve the high efficiency of visualizing, a mesh optimization algorithm based on the k-nearest neighbors (KNN) classification framework to reconstruct BIM data is proposed in place of existing mesh optimization methods that are complicated and cannot adequately handle meshes with numerous boundaries of the 3D models. In the proposed algorithm, our target BIM model is optimized with the Unity C# code based on triangle centroid concepts and classified using the KNN. As a result, the algorithm can check the number of mesh vertices and triangles before and after optimization of the entire model and each structure. In addition, it is able to optimize the mesh vertices of the original model by approximately 56 % and the triangles by about 42 %. Moreover, compared to the original model, the optimized model shows no visual differences in the model elements and information, meaning that high-performance visualization can be expected when using AR devices.

Managing the Reverse Extrapolation Model of Radar Threats Based Upon an Incremental Machine Learning Technique (점진적 기계학습 기반의 레이더 위협체 역추정 모델 생성 및 갱신)

  • Kim, Chulpyo;Noh, Sanguk
    • The Journal of Korean Institute of Next Generation Computing
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    • v.13 no.4
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    • pp.29-39
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    • 2017
  • Various electronic warfare situations drive the need to develop an integrated electronic warfare simulator that can perform electronic warfare modeling and simulation on radar threats. In this paper, we analyze the components of a simulation system to reversely model the radar threats that emit electromagnetic signals based on the parameters of the electronic information, and propose a method to gradually maintain the reverse extrapolation model of RF threats. In the experiment, we will evaluate the effectiveness of the incremental model update and also assess the integration method of reverse extrapolation models. The individual model of RF threats are constructed by using decision tree, naive Bayesian classifier, artificial neural network, and clustering algorithms through Euclidean distance and cosine similarity measurement, respectively. Experimental results show that the accuracy of reverse extrapolation models improves, while the size of the threat sample increases. In addition, we use voting, weighted voting, and the Dempster-Shafer algorithm to integrate the results of the five different models of RF threats. As a result, the final decision of reverse extrapolation through the Dempster-Shafer algorithm shows the best performance in its accuracy.