• The Journal of the Convergence on Culture Technology
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• v.10 no.4
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• pp.701-710
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• 2024

This study conducted a survey of companies in the aviation, drone, and Urban Air Mobility (UAM) sectors to analyze the educational and workforce needs, identifying essential competencies and technical training required. The study also proposed potential areas for collaboration between universities and industry regarding educational methods. Key findings and implications of the survey were derived. The results indicated that the most critical consideration for hiring was job-specific skills in the respective field. The most essential quality for workforce training was identified as enhancing the ability to use various equipment and software related to the major field. In the UAM sector, there was a high demand for personnel and education related to aircraft and components, with the highest demand being for lightweight manufacturing technology for aircraft. This study can serve as foundational data for addressing the educational needs in this field.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.241-248
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• 2023

Despite the recent short history of drones, the applying field of drones has been used for various purposes in a wide variety of areas and fields. As such, with the emergence of various types of drones over the years, in a broad sense, a remote controlled mobile object that can be controlled by wired and wireless control may be a suitable definition for drone because of various types of drones in recent years. This paper aims to help readers who want to research, develop, and use drones by examining the history, application fields, and future prospects of drones, including Unmanned Surface Vehicle(USV) and Unmanned Underwater Vehicles(UUV), as well as aerial type drones. Through this paper, it is expected that these drones will continue to be used in various fields in the future, and the prospect of future development will continue constantly. However, for the development of domestic drone technology and industry, the government's improvement in drone-related regulations should be supported.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.8
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• pp.2253-2280
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• 2024

In the upcoming era of transportation, a groundbreaking technology, known as vehicle-to-everything (V2X) communication, is poised to redefine our driving experience and revolutionize traffic management. Real-time and secure communication plays a pivotal role in V2X networks, with the decision-making process being a key factor in establishing communication and determining malicious nodes. The proposed framework utilizes a directed acyclic graph (DAG) to facilitate real-time processing and expedite decision-making. This innovative approach ensures seamless connectivity among vehicles, the surrounding infrastructure, and various entities. To enhance communication efficiency, the entire roadside unit (RSU) region can be subdivided into various sub-regions, allowing RSUs to monitor and govern each sub-region. This strategic approach significantly reduces transaction approval time, thereby improving real-time communication. The framework incorporates a consensus mechanism to ensure robust security, even in the presence of malicious nodes. Recognizing the dynamic nature of V2X networks, the addition and removal of nodes are aligned. Communication latency is minimized through the deployment of computational resources near the data source and leveraging edge computing. This feature provides invaluable recommendations during critical situations that demand swift decision-making. The proposed architecture is further validated using the "veins" simulation tool. Simulation results demonstrate a remarkable success rate exceeding 95%, coupled with a significantly reduced consensus time compared to prevailing methodologies. This comprehensive approach not only addresses the evolving requirements of secure V2X communication but also substantiates practical success through simulation, laying the foundation for a transformative era in transportation.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.23 no.4
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• pp.77-93
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• 2024

The recent development of data collection technology, which conveys various travel data in real-world such as mobile data and probe vehicle data, facilitates transportation planners identifying specified spatio-temporal travel patterns. In this study, an easily implementable travel mode classification methodology was proposed to classify inter-regional trip-modes without modeling by superimposing trajectories generated from mobile phone signaling and transportation infrastructure points into a polygon scale of a shapefile in a GIS system. Each regional mode trip was classified according to the rules such as the presence of transportation infrastructure in the trip trajectory, travel time, and the presence of access trips. An accuracy test generates Type I and Type II error results table to verify the proposed methodology. As a result, it was found that the methodology developed showed the F1-Score of the air mode 1.00, rail mode 0.95, bus mode 0.73.

• Korean Journal of Agricultural Science
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• v.49 no.4
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• pp.897-907
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• 2022

The purpose of this study is to develop a self-propelled underground crop harvester and its performance was evaluated by measuring the load during actual potato harvesting operations. This study was conducted at a constant working speed of 1 km·h^-1. A load measurement system was installed to measure the actual load and the required working power was analyzed. A hydraulic pressure sensor was also installed to measure the hydraulic pressure. The required hydraulic power was calculated using the hydraulic pressure and flow rate. The results showed that the engine speed, torque, and power during harvesting operation were in the range of 845 - 1,423 rpm, 95 - 228 Nm, and 9 - 31 kW, respectively. Traction power, excluding the hydraulic pump of the tractor and power take-off (PTO) output, was in the range of 9 - 28 kW, and it was confirmed that it occupies a ratio of 16.2 to 50% of the engine rated output. The engine can supply the minimum required traction power to move the vehicle. This means that the engine used in this study could be down-sized to be suitable for an underground crop harvester. In this study, the gear stages of the tractor were not considered. This research thus shows the possibility of developing a self-propelled underground crop harvester.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.295-304
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• 2022

The purpose of this study is to detect the sorghum panicle using YOLOv5 based on RGB images acquired by a unmanned aerial vehicle (UAV) system. The high-resolution images acquired using the RGB camera mounted in the UAV on September 2, 2022 were split into 512×512 size for YOLOv5 analysis. Sorghum panicles were labeled as bounding boxes in the split image. 2,000images of 512×512 size were divided at a ratio of 6:2:2 and used to train, validate, and test the YOLOv5 model, respectively. When learning with YOLOv5s, which has the fewest parameters among YOLOv5 models, sorghum panicles were detected with mAP@50=0.845. In YOLOv5m with more parameters, sorghum panicles could be detected with mAP@50=0.844. Although the performance of the two models is similar, YOLOv5s ( 4 hours 35 minutes) has a faster training time than YOLOv5m (5 hours 15 minutes). Therefore, in terms of time cost, developing the YOLOv5s model was considered more efficient for detecting sorghum panicles. As an important step in predicting sorghum yield, a technique for detecting sorghum panicles using high-resolution RGB images and the YOLOv5 model was presented.

• Journal of Ginseng Research
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• v.27 no.3
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• pp.103-109
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• 2003

We investigated the effect of ginseng total saponin (GTS) on the regeneration process of experimentally crush injured rat sciatic nerves. The bilateral sciatic nerves of fifty adult male Sprague-Dawley rats were compressed surgically with a straight hemostat for 30 seconds with 1 mm width. Twenty rats were divided into four groups to test the dose-dependent effect of GTS (0, 50, 100, or 150 mg/kg, i.p.). Saline for vehicle control group or GTS dissolved in saline was administerd for three weeks. After that period of time, the numbers of total myelinated axon and degenerated myelin in the sciatic nerves of bilateral legs were examined and analyzed using image analysis system to confirm a morphological effect of GTS. We found that the most effective concentration of GTS for the regeneration of damaged sciatic nerve was 150 mg/kg. In another set of experiment, thirty rats were divided into two groups as saline-treated vehicle group and GTS-treated group (150 mg/kg, i.p.) for three weeks. Every week we examined the numbers of total myelinated axon and degenerated myelin in the sciatic nerves of bilateral legs using image analysis system to evaluate the effect of GTS on injured nerves. We found that the regeneration of damaged sciatic nerves was facilitated in GTS-treated group compared to saline-treated group until two weeks. However, after that period of time we could not observe the significant difference between saline-treated group and GTS-treated group. These results suggest that GTS is a useful adjuvant therapy for the regeneration of the peripheral nerve injury in short period of treatment.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.35 no.6
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• pp.553-562
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• 2017

GPS (Global Positioning System) receiver among various sensors mounted on UAV (Unmanned Aerial Vehicle) helps to perform various functions such as hovering flight and waypoint flight based on GPS signals. GPS receiver can be used in an environment where GPS signals are smoothly received. However, recently, the use of UAV has been diversifying into various fields such as facility monitoring, delivery service and leisure as UAV's application field has been expended. For this reason, GPS signals may be interrupted by UAV's flight in a shadow area where the GPS signal is limited. Multipath can also include various noises in the signal, while flying in dense areas such as high-rise buildings. In this study, we used analytical photogrammetry and auto tracking total station technique for 3D positioning of UAV. The analytical photogrammetry is based on the bundle adjustment using the collinearity equations, which is the geometric principle of the center projection. The auto tracking total station technique is based on the principle of tracking the 360 degree prism target in units of seconds or less. In both techniques, the target used for positioning the UAV is mounted on top of the UAV and there is a geometric separation in the x, y and z directions between the targets. Data were acquired at different speeds of 0.86m/s, 1.5m/s and 2.4m/s to verify the flight speed of the UAV. Accuracy was evaluated by geometric separation of the target. As a result, there was an error from 1mm to 12.9cm in the x and y directions of the UAV flight. In the z direction with relatively small movement, approximately 7cm error occurred regardless of the flight speed.

• 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.

• Journal of Intelligence and Information Systems
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• v.28 no.1
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• pp.287-310
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• 2022

Companies' interest in developing AI-based intelligent new products is increasing. Recently, the main concern of companies is to innovate customer experience and create new values by developing new products through the effective use of Artificial intelligence technology. However, due to the nature of products based on radical technologies such as artificial intelligence, intelligent products differ from existing products and development methods, so it is clear that there is a limitation to applying the existing development methodology as it is. This study proposes a new research method based on KANO-TOPSIS for the successful development of AI-based intelligent new products by using car voice assistants as an example. Using the KANO model, select and evaluate functions that customers think are necessary for new products, and use the TOPSIS method to derives priorities by finding the importance of functions that customers need. For the analysis, major categories such as vehicle condition check and function control elements, driving-related elements, characteristics of voice assistant itself, infotainment elements, and daily life support elements were selected and customer demand attributes were subdivided. As a result of the analysis, high recognition accuracy should be considered as a top priority in the development of car voice assistants. Infotainment elements that provide customized content based on driver's biometric information and usage habits showed lower priorities than expected, while functions related to driver safety such as vehicle condition notification, driving assistance, and security, also showed as the functions that should be developed preferentially. This study is meaningful in that it presented a new product development methodology suitable for the characteristics of AI-based intelligent new products with innovative characteristics through an excellent model combining KANO and TOPSIS.