• The Journal of the Korea Contents Association
• /
• v.17 no.5
• /
• pp.262-272
• /
• 2017

This study will analyze a case that utilizes not just the characters, which are a primary licensing component in traditional animation licensing, however secondary images such as animation backgrounds, and one that has visually extended their brand. Secondary image plays an important role in developing the narration of the animation, and provides the space where the narrative takes place. It also gives important clues for the characters to be able to develop a narrative, through its chronological and geographical dimensions. This study distinguishes the components that can be used in the licensing process of an original animation into primary usage and secondary usage, and defines the scope of each. Focused on two collaboration cases, Disney's The Jungle Book & KENZO, and Disney's Alice in Wonderland & Marc by Marc Jacobs, It investigates the relationship of visual utilization between the expressive components related to secondary usage in the collaborative case of licensing, and the actual product. This study found the possibility of secondary images as a vehicle to deliver brand identity. As the spectrum of licensing elements is extended, it is necessary to develop an alternative licensing system for the new process of collaboration.

• Korean Journal of Remote Sensing
• /
• v.37 no.6_1
• /
• pp.1669-1683
• /
• 2021

AI intelligent agriculture and digital agriculture are important for the science of agriculture. Leaf chlorophyll contents(LCC) are one of the most important indicators to determine the growth status of vegetable crops. In this study, a support vector machine (SVM) regression model was produced using an unmanned aerial vehicle-based RGB camera and a multispectral (MSP) sensor for onions and garlic, and the LCC estimation applicability of the RGB camera was reviewed by comparing it with the MSP sensor. As a result of this study, the RGB-based LCC model showed lower results than the MSP-based LCC model with an average R² of 0.09, RMSE 18.66, and nRMSE 3.46%. However, the difference in accuracy between the two sensors was not large, and the accuracy did not drop significantly when compared with previous studies using various sensors and algorithms. In addition, the RGB-based LCC model reflects the field LCC trend well when compared with the actual measured value, but it tends to be underestimated at high chlorophyll concentrations. It was possible to confirm the applicability of the LCC estimation with RGB considering the economic feasibility and versatility of the RGB camera. The results obtained from this study are expected to be usefully utilized in digital agriculture as AI intelligent agriculture technology that applies artificial intelligence and big data convergence technology.

• The Journal of The Korea Institute of Intelligent Transport Systems
• /
• v.20 no.2
• /
• pp.95-111
• /
• 2021

As the population decreases in an aging society, the average age of drivers increases. Accordingly, the elderly at high risk of being in an accident need autonomous-driving vehicles. In order to secure driving safety on the road, several technologies to respond to various obstacles are required in those vehicles. Among them, technology is required to recognize static obstacles, such as poor road conditions, as well as dynamic obstacles, such as vehicles, bicycles, and people, that may be encountered while driving. In this study, we propose a deep neural network algorithm capable of simultaneously detecting these two types of obstacle. For this algorithm, we used 1,418 road images and produced annotation data that marks seven categories of dynamic obstacles and labels images to indicate road damage. As a result of training, dynamic obstacles were detected with an average accuracy of 46.22%, and road surface damage was detected with a mean intersection over union of 74.71%. In addition, the average elapsed time required to process a single image is 89ms, and this algorithm is suitable for personal mobility vehicles that are slower than ordinary vehicles. In the future, it is expected that driving safety with personal mobility vehicles will be improved by utilizing technology that detects road obstacles.

• (The) Research of the performance art and culture
• /
• no.37
• /
• pp.95-125
• /
• 2018

While myriads of small and large festivals are being organized in many regions across the country after the successful establishment of local governments around 2000, the undeniable fact is that the identity and purpose of such events are not properly reflected in their programs. This paper carefully examines the 2017 Jeonju International Sori Festival as an exemplary case of a local performing art festival to contribute to the improvement of performing art festivals in the future. In particular, it focuses on a musical program with respect to the composition, content, meaning, and direction that can effectively reveal the identity and intention of a festival. The most significant accomplishment of the 2017 Jeonju International Sori Festival is that it presented a local cultural resource, Pansori, in various ways not only to manifest its identity but also to satisfy both the enthusiasts of such musical genre and the general audience. The achievements of the 2017 Jeonju International Sori Festival through the performing art program can be summarized as follows: first, it created a new image of traditional music; second, it realized the desire to rise above regional and generational demarcations through cultural communication; third, it provided a stage for budding and local artists; fourth, it served as a vehicle for summoning the public; and last, it was conducive to expanding the spectrum of potential audience. This paper has limitation in covering the subject of the improvement of performing art festivals because it analyzed only one event. In follow-up studies, a more objective discussion should be performed by further analyzing the 2017 Jeonju International Sori Festival in comparison with various other performing art festivals.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.21 no.3
• /
• pp.419-432
• /
• 2019

Most of deep learning model training was proceeded by supervised learning, which is to train labeling data composed by inputs and corresponding outputs. Labeling data was directly generated manually, so labeling accuracy of data is relatively high. However, it requires heavy efforts in securing data because of cost and time. Additionally, the main goal of supervised learning is to improve detection performance for 'True Positive' data but not to reduce occurrence of 'False Positive' data. In this paper, the occurrence of unpredictable 'False Positive' appears by trained modes with labeling data and 'True Positive' data in monitoring of deep learning-based CCTV accident detection system, which is under operation at a tunnel monitoring center. Those types of 'False Positive' to 'fire' or 'person' objects were frequently taking place for lights of working vehicle, reflecting sunlight at tunnel entrance, long black feature which occurs to the part of lane or car, etc. To solve this problem, a deep learning model was developed by simultaneously training the 'False Positive' data generated in the field and the labeling data. As a result, in comparison with the model that was trained only by the existing labeling data, the re-inference performance with respect to the labeling data was improved. In addition, re-inference of the 'False Positive' data shows that the number of 'False Positive' for the persons were more reduced in case of training model including many 'False Positive' data. By training of the 'False Positive' data, the capability of field application of the deep learning model was improved automatically.

• Journal of the Society of Disaster Information
• /
• v.17 no.3
• /
• pp.608-616
• /
• 2021

Purpose: Safety inspections by existing personnel have been limited in evaluation and data securing due to concerns about the safety of technicians or difficulty in accessing them, and are becoming a bigger problem as the number of maintenance targets increases due to the aging of facilities. As drone technology develops, it is possible to ensure the safety of personnel, secure visual data, and diagnose quickly, and use it is increasing as safety inspection of facilities by drones was introduced recently. In order to further enhance utilization, it is considered necessary to base a consideration standard for facility appearance investigation by drones, and in this paper, research was conducted on dams. Method: To calculate the quality, existing domestic safety inspection and drone-related consideration standards were investigated, and procedures related to safety inspection using drones were compared and analyzed to review work procedures and construction types. In addition, empirical data were collected through drone photography and elevation image production for the actual dam. Result: Work types for safety inspection of facilities using drones were derived, and empirical survey results were collected for two dams according to work types. The existing guidelines were applied for the adjustment ratios for each structural type and standard of the facility, and if a meteorological reference point survey was necessary, the unmanned aerial vehicle survey of the construction work standard was applied. Conclusion: The finer the GSD in appearance investigation using drones, the greater the number of photographs taken, and the concept of adjustment cost was applied as a correction to calculate the consideration standard. In addition, it was found that the problem of maximum GSD indicating limitations should be considered in order to maintain the safe distance.

• Conservation Science in Museum
• /
• v.24
• /
• pp.55-74
• /
• 2020

The Sacred Bell of Great King Seongdeok is required digital precision recording of conservation conditions because of corrosion and partial abrasion of its patterns and inscriptions. Therefore, this study performed digital documentation of the bell using four types of scanning and unmanned aerial vehicle (UAV) photogrammetry technologies, and performed the various shape analyses through image processing. The modeling results of terrestrial laser scanning and UAV photogrammetry were merged and utilized as basic material for monitoring earthquake-induced structural deformation because these techniques can construct mutual spatial relationships between the bell and its tower. Additionally, precision scanning at a resolution four to nine times higher than that of the previous study provided highly valuable information, making it possible to visualize the patterns and inscriptions of the bell. Moreover, they are well-suited as basic data for identifying surface conservation conditions. To actively apply three-dimensional scanning results to the conservation of the original bell, the time and position of any changes in shape need to be established by further scans in the short-term. If no change in shape is detected by short-term monitoring, the monitoring should continue in medium- and long-term intervals.

• Journal of Korea Water Resources Association
• /
• v.54 no.3
• /
• pp.167-179
• /
• 2021

In order to characterize the mixing process of confluence for understanding the impacts of a river on the other river, it has been crucial to analyze the spatial mixing patterns for main streams depending on various inflow conditions of tributaries. However, most conventional studies have mostly relied upon hydraulic or water quality numerical models for understanding mixing pattern analysis of confluences, due to the difficulties to acquire a wide spatial range of in-situ data for characterizing mixing process. In this study, backscatters (or SNR) measured from ADCPs were particularly used to track sediment mixing assuming that it could be a surrogate to estimate the suspended sediment concentration. Raw backscatter data were corrected by considering the beam spreading and absorption by water. Also, an optical Laser diffraction instrument (LISST) was used to verify the method of acoustic backscatter and to collect the particle size distribution of main stream and tributary. In addition, image-based spatial distributions of sediment mixture in the confluence were monitored in various flow conditions by using an unmanned aerial vehicle (UAV), which were compared with the spatial distribution of acoustic backscatter. As results, we found that when acoustic backscatter by ADCPs were well processed, they could be proper indicators to identify the spatial patterns of the three-dimensional mixing process between two rivers. For this study, flow and sediment mixing characteristics were investigated in the confluence between Nakdong and Nam river.

• KIPS Transactions on Software and Data Engineering
• /
• v.11 no.1
• /
• pp.51-58
• /
• 2022

Drivable area detection, one of the main functions of advanced driver assistance systems, means detecting an area where a vehicle can safely drive. The drivable area detection is closely related to the safety of the driver and it requires high accuracy with real-time operation. To satisfy these conditions, V-disparity-based method is widely used to detect a drivable area by calculating the road disparity value in each row of an image. However, the V-disparity-based method can falsely detect a non-road area as a road when the disparity value is not accurate or the disparity value of the object is equal to the disparity value of the road. In a road environment including vegetation, such as a highway and a country road, the vegetation area may be falsely detected as the drivable area because the disparity characteristics of the vegetation are similar to those of the road. Therefore, this paper proposes a drivable area detection method and hardware architecture with a high accuracy in road environments including vegetation areas by reducing the number of false detections caused by V-disparity characteristic. When 289 images provided by KITTI road dataset are used to evaluate the road detection performance of the proposed method, it shows an accuracy of 90.12% and a recall of 97.96%. In addition, when the proposed hardware architecture is implemented on the FPGA platform, it uses 8925 slice registers and 7066 slice LUTs.

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