• 한국물환경학회지
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• 제21권3호
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• pp.248-255
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• 2005

Since the water quality of drinking water sources has been recognized as a big issue, the ministry of Environment in Korea is designing the total maximum daily load (TMDL) program for 4 major large rivers. The TMDL program can be successfully performed as controling the nonpoint pollutants from watershed area near the river. Of the various landuses in nonpoint pollution, parking lots and bridges are stormwater intensive landuses because of high imperviousness and high pollutant mass emissions from vehicular activities. Vehicle emissions from those areas include different pollutants such as heavy metals, oil and grease and particulates from sources such as fuels, brake pad and tire wear, etc. Especially the pollutant washed-off from the landuses are directly affecting to the river water quality. Therefore this research was conducted to understand the magnitude and nature of the stormwater emissions with the goal of quantifying stormwater pollutant concentrations and mass emission rates of pollutants from parking lot and bridges in Korea. In Kongju city areas, two monitoring sites were equipped with an automatic rainfall gages and an automatic flow meter for accumulating the useful data such as rainfall, water quality and runoff flow. This manuscripts will show the concentration changes during storm duration and EMCs to characterize the concentration profiles in different land uses. Also the first flush criteria will be suggested using dynamic EMCs. The definition of dynamic EMC is a new approach explaining the relationship of EMC and first flush effect.

• 한국산업융합학회 논문집
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• 제20권2호
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• pp.177-186
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• 2017

With the latest developments in ICT(Information Communication Technology) technology, research on Intelligent Car, Connected Car that support autonomous driving or services is actively underway. It is true that the number of inputs linked to external connections is likely to be exposed to a malicious intrusion. I studied possible security issues that may occur within the Connected Car. A variety of security issues may arise in the use of CAN, the most typical internal network of vehicles. The data can be encrypted by encrypting the entire data within the CAN network system to resolve the security issues, but can be time-consuming and time-consuming, and can cause the authentication process to be carried out in the event of a certification procedure. To resolve this problem, CAN network system can be used to authenticate nodes in the network to perform a unique authentication of nodes using nodes in the network to authenticate nodes in the nodes and By encoding the ID, identifying the identity of the data, changing the identity of the ID and decryption algorithm, and identifying the cipher and certification techniques of the external invader, the encryption and authentication techniques could be detected by detecting and verifying the external intruder. Add a monitoring node to the CAN network to resolve this. Share a unique ID that can be authenticated using the server that performs the initial certification of nodes within the network and encrypt IDs to secure data. By detecting external invaders, designing encryption and authentication techniques was designed to detect external intrusion and certification techniques, enabling them to detect external intrusions.

• 한국컴퓨터산업학회논문지
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• 제3권3호
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• pp.369-382
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• 2002

교통정체의 증가로 의하여 지불수단에 관한 관심이 증가되고 있다. 또한, 전자화폐 지불에 대한 수많은 프로젝트가 진행되고 있다. 교통시스템은 정보처리, 통신, 제어, 전자 등 다양한 첨단기술들로 구성되며 이러한 기술들을 교통관련에 접목함으로서 더욱 안전한 인명구조 및 시간과 경비절감을 더욱 효율적으로 추구할 수가 있다. 특히, 운전자의 차량소통을 위해 접촉식으로 제공하는 시스템과 이로 인해 야기되는 문제점들을 해결하기 위한 제어에 관련된 전자화폐 시스템을 연구하였다. 이 논문에서는 전자지불 개념을 ATM방식으로 도입하여 요구사항을 반영하고 기존에 개발되어 사용하고 있는 동전투입방식을 재구성하여 전체 통합하여 새로운 ITS개발에 사용하는 일련의 과정을 정리하였다. 그 결과 기존방식보다 차량대기속도 및 평균주행속도가 15%에서 40% 가량 개선됨을 확인할 수가 있었다. 특히 이런 개념은 국내ITS 개발의 특수상황에 적용하여 큰 효과를 얻을 수 있을 것으로 기대한다.

• Structural Monitoring and Maintenance
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• 제2권3호
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• pp.283-300
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• 2015

Visual condition inspections remain paramount to assessing the current deterioration status of a bridge and assigning remediation or maintenance tasks so as to ensure the ongoing serviceability of the structure. However, in recent years, there has been an increasing backlog of maintenance activities. Existing research reveals that this is attributable to the labour-intensive, subjective and disruptive nature of the current bridge inspection method. Current processes ultimately require lane closures, traffic guidance schemes and inspection equipment. This not only increases the whole-of-life costs of the bridge, but also increases the risk to the travelling public as issues affecting the structural integrity may go unaddressed. As a tool for bridge condition inspections, Unmanned Aerial Vehicles (UAVs) or, drones, offer considerable potential, allowing a bridge to be visually assessed without the need for inspectors to walk across the deck or utilise under-bridge inspection units. With current inspection processes placing additional strain on the existing bridge maintenance resources, the technology has the potential to significantly reduce the overall inspection costs and disruption caused to the travelling public. In addition to this, the use of automated aerial image capture enables engineers to better understand a situation through the 3D spatial context offered by UAV systems. However, the use of UAV for bridge inspection involves a number of critical issues to be resolved, including stability and accuracy of control, and safety to people. SLAM (Simultaneous Localisation and Mapping) is a technique that could be used by a UAV to build a map of the bridge underneath, while simultaneously determining its location on the constructed map. While there are considerable economic and risk-related benefits created through introducing entirely new ways of inspecting bridges and visualising information, there also remain hindrances to the wider deployment of UAVs. This study is to provide a context for use of UAVs for conducting visual bridge inspections, in addition to addressing the obstacles that are required to be overcome in order for the technology to be integrated into current practice.

• 터널과지하공간
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• 제29권1호
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• pp.1-11
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• 2019

본 연구에서는 국내 의료, 제조, 금융, 자동차, 도시 분야와 해외 광업 분야에서 머신러닝 기술이 활용된 사례를 조사하였다. 문헌 조사를 통해 머신러닝 기술이 의학영상 정보시스템 개발, 실시간 모니터링 및 이상 진단 시스템 개발, 정보시스템의 보안 수준 개선, 자율주행차 개발, 도시 통합관리 시스템 개발 등에 광범위하게 활용되어왔음을 알 수 있었다. 현재까지 국내 광업 분야에서는 머신러닝 기술의 활용사례를 찾을 수 없었으나, 해외에서는 광상 탐사나 광산 개발의 생산성 및 안전성을 개선을 위해 머신러닝 기술을 도입한 프로젝트들을 찾을 수 있었다. 향후 머신러닝 기술의 광업 분야 도입은 점차 확산될 것으로 예상된다.

• 한국농공학회논문집
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• 제61권4호
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• pp.1-10
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• 2019

Unmanned aerial vehicle(UAV) can acquire images with lower cost than conventional manned aircraft and commercial satellites. It has the advantage of acquiring high-resolution aerial images covering in the field area more than 50 ha. The purposes of this study is to develop the rice grain yield distribution using UAV. In order to develop a technology for estimating the rice yield using UAV images, time series UAV aerial images were taken at the paddy fields and the data were compared with the rice yield of the harvesting area for two rice varieties(Singdongjin, Dongjinchal). Correlations between the vegetation indices and rice yield were ranged from 0.8 to 0.95 in booting period. Accordingly, rice yield was estimated using UAV-derived vegetation indices($R^2=0.70$ in Sindongjin, $R^2=0.92$ in Donjinchal). It means that the rice yield estimation using UAV imagery can provide less cost and higher accuracy than other methods using combine with yield monitoring system and satellite imagery. In the future, it will be necessary to study a variety of information convergence and integration systems such as image, weather, and soil for efficient use of these information, along with research on preparing management practice work standards such as pest control and nutrient use based on UAV image information.

• Nuclear Engineering and Technology
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• 제51권3호
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• pp.807-817
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• 2019

Airborne Gamma Ray Spectrometry (AGRS) with its important applications such as gathering radiation information of ground surface, geochemistry measuring of the abundance of Potassium, Thorium and Uranium in outer earth layer, environmental and nuclear site surveillance has a key role in the field of nuclear science and human life. The Broyden-Fletcher-Goldfarb-Shanno (BFGS), with its advanced numerical unconstrained nonlinear optimization in collaboration with Artificial Neural Networks (ANNs) provides a noteworthy opportunity for modern AGRS. In this study a new AGRS system empowered by ANN-BFGS has been proposed and evaluated on available empirical AGRS data. To that effect different architectures of adaptive ANN-BFGS were implemented for a sort of published experimental AGRS outputs. The selected approach among of various training methods, with its low iteration cost and nondiagonal scaling allocation is a new powerful algorithm for AGRS data due to its inherent stochastic properties. Experiments were performed by different architectures and trainings, the selected scheme achieved the smallest number of epochs, the minimum Mean Square Error (MSE) and the maximum performance in compare with different types of optimization strategies and algorithms. The proposed method is capable to be implemented on a cost effective and minimum electronic equipment to present its real-time process, which will let it to be used on board a light Unmanned Aerial Vehicle (UAV). The advanced adaptation properties and models of neural network, the training of stochastic process and its implementation on DSP outstands an affordable, reliable and low cost AGRS design. The main outcome of the study shows this method increases the quality of curvature information of AGRS data while cost of the algorithm is reduced in each iteration so the proposed ANN-BFGS is a trustworthy appropriate model for Gamma-ray data reconstruction and analysis based on advanced novel artificial intelligence systems.

• 한국지반환경공학회 논문집
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• 제19권12호
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• pp.65-72
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• 2018

최근 무인항공시스템의 활용으로 농작물 작황조사, 접근위험지역의 시설물 현황조사, 재해재난 모니터링 및 3차원 모델링 등 그 활용 분야가 확대되고 있는 실정이며, 건설, 인프라, 영상, 측량, 농업, 감시, 수송 등 실제로 여러 분야로 활용사례가 계속 늘어나고 있다. 특히, 산사태와 같은 사면 붕괴 발생 시 무인항공시스템 적용에 대한 시도가 많아지고 있으며, 무인항공시스템은 3차원 비행이 가능하기 때문에 접근하기 어려운 공간 정보를 확인할 수 있다. 하지만, 이러한 장점에도 불구하고 사면 붕괴 발생시 무인항공시스템 활용은 아직도 제한적인 실정이다. 본 연구에서는 이러한 한계성 극복을 위하여 사면 붕괴로 인한 토사량을 무인항공시스템의 영상정보로 산정하는 기법을 고찰하였다. 본 연구를 통해 산악지역 등 접근이 어려운 지역에서 사면 붕괴 발생시 복구공사에 필요한 토사량의 정보를 취득하는데 무인항공시스템 영상정보를 활용할 수 있을 것으로 판단된다.

• 한국건축시공학회지
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• 제19권3호
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• pp.239-246
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• 2019

국내 시설물의 노후화가 급속히 진행되고 있음에 따라 정부는 노후화 시설물의 안정성 검측과 유지관리에 대한 관심을 높이고 있다. 이에 본 연구는 대구광역시 서구 비산 4동/내당 2, 3동의 노후화 지역일대를 조사하고, 비행촬영을 통해 노후화 시설물에 대한 데이터를 획득하여 3D 지도를 구현하였다. 또한 3D 지도에 객관적/주관적 데이터를 추가적으로 기입함으로써 주민들이 쉽게 이해할 수 있으며, 관리자가 노후화 시설물에 대한 유지 보수 관리를 보다 수월하게 진행할 수 있는 4D 지도 생성 방안을 제시하였다.

• Smart Structures and Systems
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• 제24권5호
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• pp.669-681
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• 2019

Bridge collapses may deliver a huge impact on our society in a very negative way. Out of many reasons why bridges collapse, poor maintenance is becoming a main contributing factor to many recent collapses. Furthermore, the aging of bridges is able to make the situation much worse. In order to prevent this unwanted event, it is indispensable to conduct continuous bridge monitoring and timely maintenance. Visual inspection is the most widely used method, but it is heavily dependent on the experience of the inspectors. It is also time-consuming, labor-intensive, costly, disruptive, and even unsafe for the inspectors. In order to address its limitations, in recent years increasing interests have been paid to the use of unmanned aerial vehicles (UAVs), which is expected to make the inspection process safer, faster and more cost-effective. In addition, it can cover the area where it is too hard to reach by inspectors. However, this strategy is still in a primitive stage because there are many things to be addressed for real implementation. In this paper, a typical procedure of bridge inspection using UAVs consisting of three phases (i.e., pre-inspection, inspection, and post-inspection phases) and the detailed tasks by phase are described. Also, three major challenges, which are related to a UAV's flight, image data acquisition, and damage identification, respectively, are identified from a practical perspective (e.g., localization of a UAV under the bridge, high-quality image capture, etc.) and their possible solutions are discussed by examining recently developed or currently developing techniques such as the graph-based localization algorithm, and the image quality assessment and enhancement strategy. In particular, deep learning based algorithms such as R-CNN and Mask R-CNN for classifying, localizing and quantifying several damage types (e.g., cracks, corrosion, spalling, efflorescence, etc.) in an automatic manner are discussed. This strategy is based on a huge amount of image data obtained from unmanned inspection equipment consisting of the UAV and imaging devices (vision and IR cameras).