• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.4
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• pp.190-211
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• 2022

This study investigated the effect of the shape and height of road signs on detection performance when detecting road signs with LiDAR, which is recognized as an essential sensor for autonomous vehicles. For the study, four types of road signs with the same area and material and different shapes were produced, and a road driving test was performed by installing a 32Ch rotating LiDAR on the upper part of the vehicle. As a result of comparing the shape of the point cloud and the NPC according to the shape of the road sign, It is expected that a distance of less than 40m is required to recognize the overall shape of a road sign using 32Ch LiDAR, and shapes such as triangles and rectangles are more advantageous than squares in securing the maximum point cloud from a long distance. As a result of the study according to the height of the road sign, At short distances (within 20m), if the height of the sign is raised to more than 2m, it deviates from the vertical viewing angle of the LiDAR and cannot express the complete point cloud shape. However, it showed a negligible effect compared to the near-field height change. These research results are expected to be utilized in the development of road facilities dedicated to LiDAR for the commercialization of autonomous cooperative driving technology.

• Journal of Platform Technology
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• v.10 no.3
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• pp.60-68
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• 2022

As AI-technology develops, interest in the safety of autonomous driving is increasing. Recently, autonomous vehicles have been increasing, but efforts to solve side effects have been sluggish. In particular, night autonomous vehicles have more problems. This is because the probability of accidents is higher in the night driving environment than in the day environment. There are more factors to consider for self-driving at night. Among these factors, reflection of light or reflected light of lighting may be a fundamental cause of night accidents. Therefore, this study proposes method to reduce accidents and improve safety by reducing reflected light generated by the headlights of opposite vehicles or various surrounding light that appear as an important problem in night autonomous vehicles. Therefore, first, in an image obtained by a sensor of a night autonomous vehicle, illumination reflected light is extracted using reflected light characteristic information, and a color of each pixel using a reflection coefficient is found to reduce a special area generated by geometric characteristics. In addition, we find a new area using only the brightness component of the specular area, define it as Illuminated Reflection Light (IRL), and finally present a method to reduce it. Although the illumination reflection light could not be completely reduce, generally satisfactory results could be obtained. Therefore, it is believed that the proposed study can reduce casualties by solving the problems of night autonomous driving and improving safety.

• Journal of Korean Society of Disaster and Security
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• v.16 no.1
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• pp.105-113
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• 2023

Jeollanam-do has 25 land-to-island and island-to-island bridges, the largest number in Korea. It is a local government rich in specialized marine and tourism resources centered on the archipelago and the sea bridges connecting them. However, in the case of sea-crossing bridges, when strong winds or typhoons occur, there is an issue that increases anxiety among users and local residents due to excessive vibration of the bridge, apart from structural safety of the bridge. In fact, in the case of Cheonsa Bridge in Shinan-gun, which was recently opened in 2019, vehicle traffic restrictions due to strong winds and excessive vibrations frequently occurred, resulting in complaints from local residents and drivers due to increased anxiety. Therefore, based on the data measured using IoT measurement technology, it is possible to relieve local residents' anxiety about the safety management of marine bridges by providing quantitative and accurate bridge vibration levels related to traffic and wind conditions of bridges in real time to local residents. This study uses the existing measurement system and IoT sensor to constantly observe the wind speed and vibration of the marine bridge, and transmits it to local residents and managers to relieve anxiety about the safety and traffic of the sea-crossing bridge, and strong winds and to develop technologies capable of preemptively responding to large-scale disasters.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.147-155
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• 2024

Currently, the 4th Industrial Revolution, like other revolutions, is bringing great change and new life to humanity, and in particular, the demand for and use of drones, which can be applied by combining various technologies such as big data, artificial intelligence, and information and communications technology, is increasing. Recently, it has been widely used to carry out dangerous military operations and missions, such as the Russia-Ukraine war and North Korea's reconnaissance against South Korea, and as the demand for and use of drones increases, concerns about the safety and security of drones are growing. Currently, a variety of research is being conducted, such as detection of wireless communication abnormalities and sensor data abnormalities related to drones, but research on real-time detection of threats using radio frequency characteristic data is insufficient. Therefore, in this paper, we conduct a study to determine whether the characteristic data is normal or abnormal signal data by collecting radio frequency signal characteristic data generated while the drone communicates with the ground control system while performing a mission in a HITL(Hardware In The Loop) simulation environment similar to the real environment. proceeded. In addition, we propose an unsupervised learning-based threat detection system and optimal threshold that can detect threat signals in real time while a drone is performing a mission.

• Journal of the Korea Organic Resources Recycling Association
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• v.32 no.1
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• pp.39-47
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• 2024

In this study, LiDAR-detective black material is synthesized by recycling silicon sludge (SS) that is generated from semiconductor manufacturing process, and its recognition is confirmed using two types of LiDAR sensors (MEMS and Rotating LiDAR). In detail, metal impurities on the surface of SS is removed, followed by coating of titanium dioxide (TiO₂) and subsequent chemical reduction to obtain SS-derived black TiO₂ (SS/bTiO₂) material. As-prepared SS/bTiO₂ is mixed with transparent paint to prepare hydrophilic black paints and applied to a glass substrate using a spray gun. SS/bTiO₂-based paint shows similar blackness (L^*=15.7) compared to commercial carbon black-based paint, and remarkable NIR reflectance (26.5R%, 905nm). Furthermore, MEMS and Rotating LiDAR have successfully detected the SS/bTiO₂-based paint. This is attributed to the occurrence of high reflection of light at the interface between the black TiO₂ and the silicon sludge according to the Fresnel's reflection principle. Hence, the new application field to effectively recycle silicon sludge generated in the semiconductor manufacturing process has been presented.

• Korean Journal of Remote Sensing
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• v.36 no.5_2
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• pp.989-1006
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• 2020

Building change monitoring based on building detection is one of the most important fields in terms of monitoring artificial structures using high-resolution multi-temporal images such as CAS500-1 and 2, which are scheduled to be launched. However, not only the various shapes and sizes of buildings located on the surface of the Earth, but also the shadows or trees around them make it difficult to detect the buildings accurately. Also, a large number of misdetection are caused by relief displacement according to the azimuth and elevation angles of the platform. In this study, object-based building detection was performed using the azimuth angle of the Sun and the corresponding main direction of shadows to improve the results of building change detection. After that, the platform's azimuth and elevation angles were used to detect changed buildings. The object-based segmentation was performed on a high-resolution imagery, and then shadow objects were classified through the shadow intensity, and feature information such as rectangular fit, Gray-Level Co-occurrence Matrix (GLCM) homogeneity and area of each object were calculated for building candidate detection. Then, the final buildings were detected using the direction and distance relationship between the center of building candidate object and its shadow according to the azimuth angle of the Sun. A total of three methods were proposed for the building change detection between building objects detected in each image: simple overlay between objects, comparison of the object sizes according to the elevation angle of the platform, and consideration of direction between objects according to the azimuth angle of the platform. In this study, residential area was selected as study area using high-resolution imagery acquired from KOMPSAT-3 and Unmanned Aerial Vehicle (UAV). Experimental results have shown that F1-scores of building detection results detected using feature information were 0.488 and 0.696 respectively in KOMPSAT-3 image and UAV image, whereas F1-scores of building detection results considering shadows were 0.876 and 0.867, respectively, indicating that the accuracy of building detection method considering shadows is higher. Also among the three proposed building change detection methods, the F1-score of the consideration of direction between objects according to the azimuth angles was the highest at 0.891.

• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.4
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• pp.327-336
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• 2019

Soybean is one of the most important crops of which the grains contain high protein content and has been consumed in various forms of food. Soybean plants are generally cultivated on the field and their yield and quality are strongly affected by climate change. Recently, the abnormal climate conditions, including heat wave and heavy rainfall, frequently occurs which would increase the risk of the farm management. The real-time assessment techniques for quality and growth of soybean would reduce the losses of the crop in terms of quantity and quality. The objective of this work was to develop a simple model to estimate the growth of soybean plant using a multispectral sensor mounted on a rotor-wing unmanned aerial vehicle(UAV). The soybean growth model was developed by using simple linear regression analysis with three phenotypic data (fresh weight, dry weight, leaf area index) and two types of vegetation indices (VIs). It was found that the accuracy and precision of LAI model using GNDVI (R²= 0.789, RMSE=0.73 ㎡/㎡, RE=34.91%) was greater than those of the model using NDVI (R²= 0.587, RMSE=1.01 ㎡/㎡, RE=48.98%). The accuracy and precision based on the simple ratio indices were better than those based on the normalized vegetation indices, such as RRVI (R²= 0.760, RMSE=0.78 ㎡/㎡, RE=37.26%) and GRVI (R²= 0.828, RMSE=0.66 ㎡/㎡, RE=31.59%). The outcome of this study could aid the production of soybeans with high and uniform quality when a variable rate fertilization system is introduced to cope with the adverse climate conditions.