• Journal of Platform Technology
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• v.11 no.3
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• pp.56-67
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• 2023

Recently, de-identification of personal information, which has been a long-cherished desire of the data-based industry, was revised and specified in August 2020. It became the foundation for activating data called crude oil[2] in the fourth industrial era in the industrial field. However, some people are concerned about the infringement of the basic rights of the data subject[3]. Accordingly, a development study was conducted on the Batch De-Identification Tool, a personal information de-identification automation tool. In this study, first, we developed an image labeling tool to label human faces (eyes, nose, mouth) and car license plates of various resolutions to build data for training. Second, an object recognition model was trained to run the object recognition module to perform de-identification of personal information. The automated personal information de-identification tool developed as a result of this research shows the possibility of proactively eliminating privacy violations through online services. These results suggest possibilities for data-based industries to maximize the value of data while balancing privacy and utilization.

• Journal of IKEEE
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• v.27 no.3
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• pp.251-257
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• 2023

In this paper, we propose a dual-structured self-attention method that improves the lack of regional features of the vision transformer's self-attention. Vision Transformers, which are more computationally efficient than convolutional neural networks in object classification, object segmentation, and video image recognition, lack the ability to extract regional features relatively. To solve this problem, many studies are conducted based on Windows or Shift Windows, but these methods weaken the advantages of self-attention-based transformers by increasing computational complexity using multiple levels of encoders. This paper proposes a dual-structure self-attention using self-attention and neighborhood network to improve locality inductive bias compared to the existing method. The neighborhood network for extracting local context information provides a much simpler computational complexity than the window structure. CIFAR-10 and CIFAR-100 were used to compare the performance of the proposed dual-structure self-attention transformer and the existing transformer, and the experiment showed improvements of 0.63% and 1.57% in Top-1 accuracy, respectively.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.107-113
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• 2023

This paper proposes a method for detecting smoking behavior in images using artificial intelligence technology. Since smoking is not a static phenomenon but an action, the object detection technology was combined with the posture estimation technology that can detect the action. A smoker detection learning model was developed to detect smokers in images, and the characteristics of smoking behaviors were applied to posture estimation technology to detect smoking behaviors in images. YOLOv8 was used for object detection, and OpenPose was used for posture estimation. In addition, when smokers and non-smokers are included in the image, a method of separating only people was applied. The proposed method was implemented using Google Colab NVIDEA Tesla T4 GPU in Python, and it was found that the smoking behavior was perfectly detected in the given video as a result of the test.

• Journal of the Korea Society of Computer and Information
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• v.14 no.6
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• pp.1-10
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• 2009

Flash memory has the advantages of nonvolatile, low power consumption, light weight, and high endurance. This enables the flash memory to be utilized as a storage of mobile computing device such as PMP(Portable Multimedia Player). Potable device with a mass flash memory can store various multimedia data such as video, audio, or image. Typical index systems for mobile computer are inefficient to search a form of text like lyric or title. In this paper, we propose a new text index system, named EMTEX(Embedded Text Index). EMTEX has the following salient features. First, it uses a compression algorithm for embedded system. Second, if a new insert or delete operation is executed on the base table. EMTEX updates the text index immediately. Third, EMTEX considers the characteristics of flash memory to design insert, delete, and rebuild operations on the text index. Finally, EMTEX is executed as an upper layer of DBMS. Therefore, it is independent of the underlying DBMS. We evaluate the performance of EMTEX. The Experiment results show that EMTEX can outperform th conventional index systems such as Oracle Text and FT3.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.1-6
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• 2022

In this research, we suggest a real-time tele-driving system for unmanned fire truck control using the LTE communication system. The operator located in the safe area could drive the unmaned fire truck by implementing the secure tele-operation in case of the emergencies and disaster situation. A prototype of the unmaned fire truck was developed with a fire canon, a high pressure pump, a ball valve and a horse reel. The effect of time delay and FPS was quantified depending on the image sizes and the effective system for realtime tele-operation was suggested. To verify the suggested system, the test was performed between an operator and an unmanned fire truck which is approximately 30km apart. In this research, the immersion tele-driving system is suggested for real-time fire suppression with a 120ms time delay using LTE communication.

• Journal of Animal Science and Technology
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• v.65 no.3
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• pp.519-534
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• 2023

Rumination in cattle is closely related to their health, which makes the automatic monitoring of rumination an important part of smart pasture operations. However, manual monitoring of cattle rumination is laborious and wearable sensors are often harmful to animals. Thus, we propose a computer vision-based method to automatically identify multi-object cattle rumination, and to calculate the rumination time and number of chews for each cow. The heads of the cattle in the video were initially tracked with a multi-object tracking algorithm, which combined the You Only Look Once (YOLO) algorithm with the kernelized correlation filter (KCF). Images of the head of each cow were saved at a fixed size, and numbered. Then, a rumination recognition algorithm was constructed with parameters obtained using the frame difference method, and rumination time and number of chews were calculated. The rumination recognition algorithm was used to analyze the head image of each cow to automatically detect multi-object cattle rumination. To verify the feasibility of this method, the algorithm was tested on multi-object cattle rumination videos, and the results were compared with the results produced by human observation. The experimental results showed that the average error in rumination time was 5.902% and the average error in the number of chews was 8.126%. The rumination identification and calculation of rumination information only need to be performed by computers automatically with no manual intervention. It could provide a new contactless rumination identification method for multi-cattle, which provided technical support for smart pasture.

• Journal of Industrial Convergence
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• v.21 no.8
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• pp.13-21
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• 2023

The purpose of this study was to compare storytelling with TV entertainment programs to find out which elements of web entertainment affected the shift of viewers' interest, and to identify the characteristics of web entertainment storytelling. To this end, each of the web and TV entertainment programs were selected for storytelling analysis, and storytelling analyzed the contents of each item by dividing them into images, backgrounds, stories, and characters. As a result of the analysis, unlike TV programs, web entertainment storytelling allows viewers to immerse themselves in content through a composition that runs directly from the beginning to the crisis, and is characterized by a clear formation in a short video through a clear ending narrative. These research results hope that short-form web entertainment programs produced in the future will be able to identify strategies for viewers' immersion and storytelling strategies.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.89-89
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• 2022

Soybean (Glycine max (L.) Merr.) is a representative food resource. To preserve the integrity of soybean, it is necessary to protect soybean yield and seed quality from threats of various pests and diseases. Riptortus pedestris is a well-known insect pest that causes the greatest loss of soybean yield in South Korea. This pest not only directly reduces yields but also causes disorders and diseases in plant growth. Unfortunately, no resistant soybean resources have been reported. Therefore, it is necessary to identify the distribution and movement of Riptortus pedestris at an early stage to reduce the damage caused by insect pests. Conventionally, the human eye has performed the diagnosis of agronomic traits related to pest outbreaks. However, due to human vision's subjectivity and impermanence, it is time-consuming, requires the assistance of specialists, and is labor-intensive. Therefore, the responses and behavior patterns of Riptortus pedestris to the scent of mixture R were visualized with a 3D model through the perspective of artificial intelligence. The movement patterns of Riptortus pedestris was analyzed by using time-series image data. In addition, classification was performed through visual analysis based on a deep learning model. In the object tracking, implemented using the YOLO series model, the path of the movement of pests shows a negative reaction to a mixture Rina video scene. As a result of 3D modeling using the x, y, and z-axis of the tracked objects, 80% of the subjects showed behavioral patterns consistent with the treatment of mixture R. In addition, these studies are being conducted in the soybean field and it will be possible to preserve the yield of soybeans through the application of a pest control platform to the early stage of soybeans.

• Journal of Internet of Things and Convergence
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• v.10 no.2
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• pp.25-41
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• 2024

For the scenario of open pit mining, at present, manual periodic verification is mainly carried out in China with the help of video surveillance, which requires continuous investment in labor cost and has poor timeliness. In order to solve this difficult problem of early warning and monitoring, this paper researches a spatialized algorithmic model and designs an early warning system for open-pit mine transboundary mining, which is realized by calculating the coordinate information of the mining and extracting equipments and comparing it with the layer coordinates of the approval range of the mines in real time, so as to realize the determination of the transboundary mining behavior of the mines. By taking the Pingxiang area of Jiangxi Province as the research object, after the field experiment, it shows that the system runs stably and reliably, and verifies that the target tracking accuracy of the system is high, which can effectively improve the early warning capability of the open-pit mines' overstepping the boundary, improve the timeliness and accuracy of mine supervision, and reduce the supervision cost.

• The Journal of the Acoustical Society of Korea
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• v.28 no.2
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• pp.141-147
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• 2009

This paper presents a new fast algorithm, rotation-based method (RBM), for the reconstruction of 3 dimensional image for cone beam computerized tomography (CB CT) system. The system used cone beam has less exposure time of radioactivity than fan beam. The Three-Pass Shear Matrices (TPSM) is applied, that has less transcendental functions than the one-pass shear method to decrease a time of calculations in the computer. To evaluate the quality of the 3-D images and the time for the reconstruction of the 3-D images, another 3-D images were reconstructed by the radon transform under the same condition. For the quality of the 3-D images, the images by radon transform was shown little good quality than REM. But for the time for the reconstruction of the 3-D images REM algorithm was 35 times faster than radon transform. This algorithm offered $4{\sim}5$ frames a second. It meant that it will be possible to reconstruct the 3-D dynamic images in real time.