• Journal of Positioning, Navigation, and Timing
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• v.6 no.4
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• pp.159-166
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• 2017

The position of autonomous driving vehicle is basically acquired through the global positioning system (GPS). However, GPS signals cannot be received in tunnels. Due to this limitation, localization of autonomous driving vehicles can be made through sensors mounted on them. In particular, a 3D Light Detection and Ranging (LIDAR) system is used for longitudinal position error correction. Few feature points and structures that can be used for localization of vehicles are available in tunnels. Since lanes in the road are normally marked by solid line, it cannot be used to recognize a longitudinal position. In addition, only a small number of structures that are separated from the tunnel walls such as sign boards or jet fans are available. Thus, it is necessary to extract usable information from tunnels to recognize a longitudinal position. In this paper, fire hydrants and evacuation guide lights attached at both sides of tunnel walls were used to recognize a longitudinal position. These structures have highly distinctive reflectivity from the surrounding walls, which can be distinguished using LIDAR reflectivity data. Furthermore, reflectivity information of tunnel walls was fused with the road surface reflectivity map to generate a synthetic reflectivity map. When the synthetic reflectivity map was used, localization of vehicles was able through correlation matching with the local maps generated from the current LIDAR data. The experiments were conducted at an expressway including Maseong Tunnel (approximately 1.5 km long). The experiment results showed that the root mean square (RMS) position errors in lateral and longitudinal directions were 0.19 m and 0.35 m, respectively, exhibiting precise localization accuracy.

• Journal of the Korea institute for structural maintenance and inspection
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• v.26 no.5
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• pp.30-42
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• 2022

Due to the increase in construction of tunnels, the burdens of maintenance works for tunnel structures have been increasing in Korea. In addition, the increase of traffic volume and aging of materials also threatens the safety of tunnel facilities, therefore, maintenance costs are expected to increase significantly in the future. Accordingly, automated condition assessment technologies like image-based tunnel scanning system for inspection and diagnosis of tunnel facilities have been proposed. For image-based tunnel scanning system, it is key to create a planar image through stitching of multiple images captured by tunnel scanning system. In this study, performance of feature-based stitching algorithms suitable for stitching tunnel scanning images was evaluated. In order to find a suitable algorithm SIFT, ORB, and BRISK are compared. The performance of the proposed algorithm was determined by the number of feature extraction, calculation speed, accuracy of feature matching, and image stitching result. As for stitching performance, SIFT algorithm was the best in all parts of tunnel image. ORB and BRISK also showed satisfactory performance and short calculation time. SIFT can be used to generate precise planar images. ORB and BRISK also showed satisfactory stitching results, confirming the possibility of being used when real-time stitching is required.

• Journal of the Korean Geotechnical Society
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• v.40 no.1
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• pp.15-28
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• 2024

This paper proposes a method to acquire image data inside tunnel structures and a method to structure the acquired image data. By improving the conditions by which image data are acquired inside the tunnel structure, high-quality image data can be obtained from area type tunnel scanning. To improve the data acquisition conditions, a longitudinal rail of the tunnel can be installed on the tunnel ceiling, and image data of the entire tunnel structure can be acquired by moving the installed rail. This study identified 0.5 mm cracked simulation lines under a distance condition of 20 m at resolutions of 3,840 × 2,160 and 720 × 480 pixels. In addition, the proposed image-data-structuring method could acquire image data in image tile units. Here, the image data of the tunnel can be structured by substituting the application factors (resolution of the acquired image and the tunnel size) into a relationship equation. In an experiment, the image data of a tunnel with a length of 1,000 m and a width of 20 m were obtained with a minimum overlap rate of 0.02% to 8.36% depending on resolution and precision, and the size of the local coordinate system was found to be (14 × 15) to (36 × 34) pixels.

• Journal of the Korean earth science society
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• v.23 no.1
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• pp.15-29
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• 2002

Bayesian inversion for gravity and resistivity data was performed to investigate the cavity structure appearing as a lava tunnel in Cheju Island, Korea. Dipole-dipole DC resistivity data were proposed for a prior information of gravity data and we applied the geostatistical techniques such as kriging and simulation algorithms to provide a prior model information and covariance matrix in data domain. The inverted resistivity section gave the indicator variogram modeling for each threshold and it provided spatial uncertainty to give a prior PDF by sequential indicator simulations. We also presented a more objective way to make data covariance matrix that reflects the state of the achieved field data by geostatistical technique, cross-validation. Then Gaussian approximation was adopted for the inference of characteristics of the marginal distributions of model parameters and Broyden update for simple calculation of sensitivity matrix and SVD was applied. Generally cavity investigation by geophysical exploration is difficult and success is hard to be achieved. However, this exotic multiple interpretations showed remarkable improvement and stability for interpretation when compared to data-fit alone results, and suggested the possibility of diverse application for Bayesian inversion in geophysical inverse problem.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.6 no.3
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• pp.237-246
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• 1994

Experimental data are reported for charging and collection of NaCl aerosols in the 0.03- to $0.2{\mu}m$-geometric-mean-diameter range in 2-stage parallel-plate electrostatic precipitators. The NaCl aerosols are generated with geometric standard deviation of about 1.74 and particle generation rate of about 10^9 particles/see by the constant output atomizer and injected into the air flow in the clean wind-tunnel. The 2-stage parallel-plate electrostatic precipitator installed in the test section of the wind-tunnel is operated with a positive corona discharge. The NaCl aerosols in the channel flow are sampled and transported to the aerosol particle number concentration measurement system by using the isoaxial sampling and transport system constructed based on the Okazaki and Willeke design. The aerosol particle number concentration measurement system measures the size distribution of submicrometer aerosols by an electrical mobility detection technique. It is confirmed from comparing the measured collection efficiencies in this study and the predicted ones by our previous theoretical analysis that the predicted collection efficiencies agree well with the experimental ones. It is also found from the comparison that below about $0.02{\mu}m$ all particles are not charged and the uncharged particles are not collected, and consequently 2-stage parallel-plate electrostatic precipitators are not suitable for that particle size range.

• Tunnel and Underground Space
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• v.33 no.6
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• pp.561-573
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• 2023

Korea Atomic Energy Research Institute (KAERI) constructed the KURT (KAERI Underground Research Tunnel) to analyze the hydrogeological/geochemical characteristics of deep rock mass. Numerous boreholes have been drilled to conduct various field tests. The selection of suitable investigation intervals within a borehole is of great importance. When objectives are centered around hydraulic flow and groundwater sampling, intervals with sufficient groundwater flow are the most suitable. This study defines such points as hydraulic outliers and aimed to detect them using borehole geophysical logging data (temperature and EC) from a 1 km depth borehole. For systematic and efficient outlier detection, machine learning algorithms, such as DBSCAN, OCSVM, kNN, and isolation forest, were applied and their applicability was assessed. Following data preprocessing and algorithm optimization, the four algorithms detected 55, 12, 52, and 68 outliers, respectively. Though this study confirms applicability of the machine learning algorithms, it is suggested that further verification and supplements are desirable since the input data were relatively limited.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.3-4
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• 2021

폐쇄된 터널 내부에서는 사고가 일어날 경우 외부에서는 터널 내 상황을 알 수가 없어 경미한 사고라 하더라도 대형 후속 2차 사고로 이어질 가능성이 크다. 또한영상탐지로사고 상황의 오검출을 줄이기 위해서, 본 연구에서는기존의 많은 CNN 모델 중 보유한 데이터에 가장 적합한 모델을 선택하는 과정에서 가장 좋은 성능을 보인 VGG16 모델을 전이학습 시키고 fully connected layer의 일부 layer에 Dropout을 적용시켜 Overfitting을일부방지하는 CNN 모델을 생성한 뒤Yolo를 이용한 영상 내 객체인식, OpenCV를 이용한 영상 프레임 내에서 객체의ROI를 추출하고이를 CNN 모델과 비교하여오검출을 줄이면서 사고를 검출하는 시스템을 제안하였다.

• Smart Structures and Systems
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• v.6 no.3
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• pp.239-262
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• 2010

Tube Lines has carried out a "knowledge and investigation programme" on the deep tube tunnels comprising the Jubilee, Northern and Piccadilly lines, as required by the PPP contract with London Underground. Many of the tunnels have been in use for over 100 years, so this assessment was considered essential to the future safe functioning of the system. This programme has involved a number of generic investigations which guide the assessment methodology and the analysis of some 5,000 individual structures. A significant amount of investigation has been carried out, including ultrasonic thickness measurement, detection of brickwork laminations using radar, stress measurement using magnetic techniques, determination of soil parameters using CPT, pressuremeter and laboratory testing, installation of piezometers, material and tunnel segment testing, and trialling of remote photographic techniques for inspection of large tunnels and shafts. Vibrating wire, potentiometer, electro level, optical and fibre-optic monitoring has been used, and laser measurement and laser scanning has been employed to measure tunnel circularity. It is considered that there is scope for considerable improvements in non-destructive testing technology for structural assessment in particular, and some ideas are offered as a "wish-list". Assessment reports have now been produced for all assets forming Tube Lines' deep tube tunnel network. For assets which are non-compliant with London Underground standards, the risk to the operating railway has to be maintained as low as reasonably practicable (ALARP) using enhanced inspection and monitoring, or repair where required. Monitoring techniques have developed greatly during recent years and further advances will continue to support the economic whole life asset management of infrastructure networks.

• Tunnel and Underground Space
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• v.30 no.1
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• pp.76-86
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• 2020

In this study, a small autonomous driving robot was developed for underground mines using the Light Detection and Ranging (LiDAR) sensor. The developed robot measures the distances to the left and right wall surfaces using the LiDAR sensor, and automatically controls its steering to drive along the centerline of mine tunnel. A field experiment was conducted in an underground amethyst mine to test the driving performance of developed robot. During five repeated driving tests, the robot showed stable driving performance overall. There were no collision accidents with the wall of mine tunnel.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.6D
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• pp.931-936
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• 2008

Surveying by using terrestrial LiDAR(Light Detection And Ranging) is more rapid than by using total station which enables tunnel section profile surveying to be done in suitable time and minimize centerline error, occurrence of overcut and undercut. Therefore, utilization of terrestrial LiDAR has increased more and more in section profile survey and measurement field Moreover, studies of terrestrial LiDAR for accurate and efficient utilization is now ongoing vigorously. Average end area formula, which was generally used to calculate overcut and undercut, was compared with existing methods such as total station survey and photogrammetry. However, there are no criteria of spacing distance for calculating overcut and undercut through terrestrial LiDAR surveying which can acquire 3D information of whole tunnel. This research performed reverse engineering to decide optimal spacing distance when surveying tunnel section profile by comparing whole tunnel volume and tunnel volume in difference spacing distance. This result was utilized to produce CAD drawing for the test tunnel site where there is no design drawings. In addition to this, efficiency of LiDAR and accuracy of CAD drawing was compared with targetless total station surveying of tunnel section profile. Finally, error analysis of target coordinate's accuracy and incidence angle was done in order to verify the accuracy of terrestrial LiDAR technology.