• The Journal of the Society of Korean Medicine Diagnostics
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• v.20 no.1
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• pp.15-26
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• 2016

Objectives Syndrome differentiation and treatment (辨證論治) was one of the core theories in Korean medicine and syndrome differentiation (辨證) constitutes a branch of disease diagnosis in Korean medicine. Yoon Gil-Young, one of the modern outstanding scholar of basic medical science in Korean medicine, wrote on basic theories of Korean medicine such as physiology, pathology, formula science, etc. Hereby we will analyze and discuss his works to understand his recognition of historical changes in the syndrome differentiation. Methods We conducted researches into the two works of Yoon Gil-Young's, which are "The Clinical Formula Science of Eastern Medicine (東醫臨床方劑學)" and "The theory of Four-Constitution Medicine (四象體質醫學論)". From Yoon's academic standpoint which connects the basic medical science with the clinical medicine, we analyzed his opinion about the system in the Theory of 'Syndrome Differentiation'. Results According to Yoon's research work on the Theory of 'Syndrome Differentiation', the system of syndrome differentiation, which had its deep root in the theory of Yin and Yang (陰陽) & the theory of abbreviation of the five circuit phases (五運) and the six atomspheric influences (六氣) of the "Huangdi's Internal Classic (黃帝內經)". Conclusions Yoon Gil-Young's theory of differentiation of syndromes and treatment is widespread so much that he studied on the learning field of Traditional Korean Mediciine and ingenious as well. He explain on the main principles of differentiation of syndromes based on "Huang Di Nei Jing" and the system of differentiation of syndromes is composed of Traditional Korean Medical Physiology.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.5
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• pp.1001-1010
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• 2021

Normal people are not deeply aware of their dependence on sight when eating. However, since the visually impaired do not know what kind of food is on the table, the assistant next to them holds the blind spoon and explains the position of the food in a clockwise direction, front and rear, left and right, etc. In this paper, we describe the development of a meal assistance system that recognizes each food image and announces the name of the food by voice when a visually impaired person looks at their table using a smartphone camera. This system extracts the food on which the spoon is placed through the YOLO model that has learned the image of food and tableware (spoon), recognizes what the food is, and notifies it by voice. Through this system, it is expected that the visually impaired will be able to eat without the help of a meal assistant, thereby increasing their self-reliance and satisfaction.

• Journal of Digital Convergence
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• v.16 no.12
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• pp.301-307
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• 2018

The most of the IR focus on the method for searching the document, so the keyword-based IR system is not able to reflect the feature information of the image. In order to overcome these limitations, we have developed a system that can search similar images based on the vector information of images, and it can search for similar images based on sketches. The proposed system uses the GAN to up sample the sketch to the image level, convert the image to the vector through the CNN, and then retrieve the similar image using the vector space model. The model was learned using fashion image and the image retrieval system was developed. As a result, the result is showed meaningful performance.

• Convergence Security Journal
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• v.21 no.1
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• pp.93-100
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• 2021

Damage caused by intelligent cyber attacks not only disrupts system operations and leaks information, but also entails massive economic damage. Recently, cyber attacks have a distinct goal and use advanced attack tools and techniques to accurately infiltrate the target. In order to minimize the damage caused by such an intelligent cyber attack, it is necessary to block the cyber attack at the beginning or during the attack to prevent it from invading the target's core system. Recently, technologies for predicting cyber attack paths and analyzing risk level of cyber attack using big data or artificial intelligence technologies are being studied. In this paper, a cyber attack path analysis method using attack tree and RFI is proposed as a basic algorithm for the development of an automated cyber attack path prediction system. The attack path is visualized using the attack tree, and the priority of the path that can move to the next step is determined using the RFI technique in each attack step. Based on the proposed mechanism, it can contribute to the development of an automated cyber attack path prediction system using big data and deep learning technology.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.1
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• pp.63-68
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• 2021

Recently there is sharply increasing interest in watching and creating video contents such as YouTube. However, creating such video contents without privacy protection technique can expose other people in the background in public, which is consequently violating their privacy rights. This paper seeks to remedy these problems and proposes a technique that identifies faces and protecting portrait rights by blurring the face. The key contribution of this paper lies on our deep-learning technique with low detection error and high computation that allow to protect portrait rights in real-time videos. To reduce errors, an efficient tracking algorithm was used in this system with face detection and face recognition algorithm. This paper compares the performance of the proposed system with and without the tracking algorithm. We believe this system can be used wherever the video is used.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.1
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• pp.22-27
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• 2020

Recently, as the number of households raising pets increases due to the increase of single households, there is a need for a system for monitoring the status or behavior of pets. There are regional limitations in the monitoring of pets using domestic CCTVs, which requires a large number of CCTVs or restricts the behavior of pets. In this paper, we propose a mobile system for detecting and tracking cats using deep learning to solve the regional limitations of pet monitoring. We use YOLO (You Look Only Once), an object detection neural network model, to learn the characteristics of pets and apply them to Raspberry Pi to track objects detected in an image. We have designed a mobile monitoring system that connects Raspberry Pi and a laptop via wireless LAN and can check the movement and condition of cats in real time.

• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.19-26
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• 2022

With the recent development of intelligent transportation systems, car license plate recognition systems are being used in various fields. Such systems need to guarantee real-time performance to recognize the license plate of a driving car. Also, they should keep a high recognition rate even in problematic situations such as small license plates in low-resolution and unclear image due to distortion. In this paper, we propose a real-time car license plate recognition system that improved processing speed using object detection algorithm based on anchor-free method and text recognition algorithm based on Convolutional Neural Network(CNN). In addition, we used Spatial Transformer Network to increase the recognition rate on the low resolution or distorted images. We confirm that the proposed system is faster than previously existing car license plate recognition systems and maintains a high recognition rate in a variety of environment and quality images because the proposed system's recognition rate is 93.769% and the processing speed per image is about 0.006 seconds.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.6
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• pp.1171-1181
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• 2022

Front Collision-Avoidance Assist (FCA) or Smart Cruise Control (SCC) is installed in a modern vehicle, and the amount of data exchange between ECUs increases rapidly. Therefore, Automotive Ethernet, especially SOME/IP, which supports wide bandwidth and two-way communication, is widely adopted to overcome the bandwidth limitation of traditional CAN communication. SOME/IP is a standard protocol compatible with various automobile operating systems, and improves connectivity between components in the vehicle. However, no encryption or authentication process is defined in the SOME/IP protocol itself. Therefore, there is a need for a security study on the SOME/IP protocol. This paper proposes a deep learning-based intrusion detection system in SOME/IP and performs six attacks to confirm the performance of the intrusion detection system.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.2
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• pp.313-320
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• 2023

The cumulative registration status of dogs is from 1.07 million in 2016 to 2.32 million in 2020. Animal registration is increasing by more than 10% every year, and accordingly, a name must be decided when registering a dog. We want to give a name that fits the characteristics of a dog's appearance, but there are many difficulties in naming it. This paper explains the development of a system for recognizing dog images and recommends dog names based on similar objects or food. This system extracts similarities with dogs' images through models that learn images of various objects and foods, and recommends dog names based on similarities. In addition, by recommending additional related words based on the image data of the result value, it was possible to provide users with various options, increase convenience, and increase interest and fun. Through this system, it is expected that users will be able to solve their concerns about naming their dogs, check names that suit their dogs comfortably, and give them various options through various recommended names to increase satisfaction.

• Journal of the Society of Naval Architects of Korea
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• v.61 no.5
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• pp.324-333
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• 2024

This paper focuses on the cooperation between Unmanned Aerial Vehicle (UAV) and Unmanned Surface Vessel (USV). It aims to develop efficient guidance and control algorithms for USV based on obstacle identification and path planning from aerial images captured by UAV. Various obstacle scenarios were implemented using the Robot Operating System (ROS) and the Gazebo simulation environment. The aerial images transmitted in real-time from UAV to USV are processed using the computer vision-based deep learning model, You Only Look Once (YOLO), to classify and recognize elements such as the water surface, obstacles, and ships. The recognized data is used to create a two-dimensional grid map. Algorithms such as A^* and Rapidly-exploring Random Tree star (RRT^*) were used for path planning. This process enhances the guidance and control strategies within the UAV-USV collaborative system, especially improving the navigational capabilities of the USV in complex and dynamic environments. This research offers significant insights into obstacle avoidance and path planning in maritime environments and proposes new directions for the integrated operation of UAV and USV.